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• Getting Started with StreamInsight 2.1

  - by Roman Schindlauer

  If you're just beginning to get familiar with StreamInsight, you may be looking for a way to get started. What are the basics? How can I get my first StreamInsight application running so I can see how it works? Where is the 'front door' that will get me going? If that describes you, then this blog entry might be just what you need. If you're already a StreamInsight wiz, keep reading anyway - you may find some helpful links here that you weren't aware of. But here's what we'd like from you experienced readers in particular: if you know of other good resources that we missed, please feel free to add them in the comments below. We appreciate you sharing your expertise. The Book The basic documentation for StreamInsight is located in the MSDN Library (Microsoft StreamInsight 2.1). You'll notice that previous versions of StreamInsight are still there (1.2 and 2.0), but if you're just getting started you can stick to the 2.1 section. The documentation has been organized to function as reference material, which is fine after you're familiar with the technology. But if you're trying to learn the basics, you might want to take a different path instead of just starting at the top. The following is one map you can use. What Is StreamInsight? Here is a sequence of topics that should give you a good overview of what StreamInsight is and how it works: Overview answers the question, "what is it?" StreamInsight Server Architecture gives you a quick look at a high-level architectural drawing StreamInsight Concepts lays out an overview of the basic components Deploying StreamInsight Entities to a StreamInsight Server describes the mechanics of how these components work together Getting an Example Running Once you have this background, go ahead and install StreamInsight and get a basic example up and running: Installation download and install the software StreamInsight Examples walk through a set of 3 simple StreamInsight applications that work together to demonstrate what you learned in the topics above; you can copy and paste the code into Visual Studio, compile, and run That's it - you now have a real, functioning StreamInsight system! Now that you have a handle on the basics, you might want to start digging deeper. Digging Deeper Here's a suggested path through the documentation to help you understand the next layer of StreamInsight technologies: Using Event Sources and Event Sinks sources supply data and sinks consume it; this topic gives you an overview of how they work Publishing and Connecting to the StreamInsight Server practical details on how to set up a StreamInsight server A Hitchhiker’s Guide to StreamInsight 2.1 Queries queries are the heart of how StreamInsight performs data analytics, and this whitepaper will help you really understand how they work Using StreamInsight LINQ root through this section for technical details on specific query components Using the StreamInsight Event Flow Debugger in addition to troubleshooting, the debugger is a great way to learn more about what goes on inside a StreamInsight application And Even Deeper Finally, to get a handle on some of the more complex things you can do with StreamInsight, dig into these: Input and Output Adapters adapters can be useful for handling more complex sources and sinks Building Resilient StreamInsight Applications a resilient application is able to recover from system failures Operations this section will help you monitor and troubleshoot a running StreamInsight system The StreamInsight Community As you're designing and developing your StreamInsight solutions, you probably will find it helpful to see working examples or to learn tips and tricks from others. Or maybe you need a place to post a vexing question. Here are some community resources that we have found useful. If you know of others, please add them in the comments below. Code samples and tools Official StreamInsight code samples Introduction to LinqPad Driver for StreamInsight 2.1 - LinqPad is a very useful tool for developing queries The following case studies are based on earlier versions of StreamInsight, but they still are useful examples: Microsoft Media Analytics - real-time monitoring and analytic Edgenet - responding to information from multiple source ICONICS - managing energy usage Blogs Microsoft StreamInsight Ruminations of J.net Richard Seroter's Architecture Musings pluralsight Forums MSDN StreamInsight Forum stackoverflow Training Microsoft StreamInsight Fundamentals (“Introducing StreamInsight” is free) from pluralsight Twitter @streaminsight   You’re a StreamInsight Expert That should get you going. Please add any other resources you have found useful in the comments below.   Regards, The StreamInsight Team

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• Taming Hopping Windows

  - by Roman Schindlauer

  At first glance, hopping windows seem fairly innocuous and obvious. They organize events into windows with a simple periodic definition: the windows have some duration d (e.g. a window covers 5 second time intervals), an interval or period p (e.g. a new window starts every 2 seconds) and an alignment a (e.g. one of those windows starts at 12:00 PM on March 15, 2012 UTC). var wins = xs     .HoppingWindow(TimeSpan.FromSeconds(5),                    TimeSpan.FromSeconds(2),                    new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc)); Logically, there is a window with start time a + np and end time a + np + d for every integer n. That’s a lot of windows. So why doesn’t the following query (always) blow up? var query = wins.Select(win => win.Count()); A few users have asked why StreamInsight doesn’t produce output for empty windows. Primarily it’s because there is an infinite number of empty windows! (Actually, StreamInsight uses DateTimeOffset.MaxValue to approximate “the end of time” and DateTimeOffset.MinValue to approximate “the beginning of time”, so the number of windows is lower in practice.) That was the good news. Now the bad news. Events also have duration. Consider the following simple input: var xs = this.Application                 .DefineEnumerable(() => new[]                     { EdgeEvent.CreateStart(DateTimeOffset.UtcNow, 0) })                 .ToStreamable(AdvanceTimeSettings.IncreasingStartTime); Because the event has no explicit end edge, it lasts until the end of time. So there are lots of non-empty windows if we apply a hopping window to that single event! For this reason, we need to be careful with hopping window queries in StreamInsight. Or we can switch to a custom implementation of hopping windows that doesn’t suffer from this shortcoming. The alternate window implementation produces output only when the input changes. We start by breaking up the timeline into non-overlapping intervals assigned to each window. In figure 1, six hopping windows (“Windows”) are assigned to six intervals (“Assignments”) in the timeline. Next we take input events (“Events”) and alter their lifetimes (“Altered Events”) so that they cover the intervals of the windows they intersect. In figure 1, you can see that the first event e1 intersects windows w1 and w2 so it is adjusted to cover assignments a1 and a2. Finally, we can use snapshot windows (“Snapshots”) to produce output for the hopping windows. Notice however that instead of having six windows generating output, we have only four. The first and second snapshots correspond to the first and second hopping windows. The remaining snapshots however cover two hopping windows each! While in this example we saved only two events, the savings can be more significant when the ratio of event duration to window duration is higher. Figure 1: Timeline The implementation of this strategy is straightforward. We need to set the start times of events to the start time of the interval assigned to the earliest window including the start time. Similarly, we need to modify the end times of events to the end time of the interval assigned to the latest window including the end time. The following snap-to-boundary function that rounds a timestamp value t down to the nearest value t' <= t such that t' is a + np for some integer n will be useful. For convenience, we will represent both DateTime and TimeSpan values using long ticks: static long SnapToBoundary(long t, long a, long p) {     return t - ((t - a) % p) - (t > a ? 0L : p); } How do we find the earliest window including the start time for an event? It’s the window following the last window that does not include the start time assuming that there are no gaps in the windows (i.e. duration < interval), and limitation of this solution. To find the end time of that antecedent window, we need to know the alignment of window ends: long e = a + (d % p); Using the window end alignment, we are finally ready to describe the start time selector: static long AdjustStartTime(long t, long e, long p) {     return SnapToBoundary(t, e, p) + p; } To find the latest window including the end time for an event, we look for the last window start time (non-inclusive): public static long AdjustEndTime(long t, long a, long d, long p) {     return SnapToBoundary(t - 1, a, p) + p + d; } Bringing it together, we can define the translation from events to ‘altered events’ as in Figure 1: public static IQStreamable<T> SnapToWindowIntervals<T>(IQStreamable<T> source, TimeSpan duration, TimeSpan interval, DateTime alignment) {     if (source == null) throw new ArgumentNullException("source");     // reason about DateTime and TimeSpan in ticks     long d = Math.Min(DateTime.MaxValue.Ticks, duration.Ticks);     long p = Math.Min(DateTime.MaxValue.Ticks, Math.Abs(interval.Ticks));     // set alignment to earliest possible window     var a = alignment.ToUniversalTime().Ticks % p;     // verify constraints of this solution     if (d <= 0L) { throw new ArgumentOutOfRangeException("duration"); }     if (p == 0L || p > d) { throw new ArgumentOutOfRangeException("interval"); }     // find the alignment of window ends     long e = a + (d % p);     return source.AlterEventLifetime(         evt => ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p)),         evt => ToDateTime(AdjustEndTime(evt.EndTime.ToUniversalTime().Ticks, a, d, p)) -             ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p))); } public static DateTime ToDateTime(long ticks) {     // just snap to min or max value rather than under/overflowing     return ticks < DateTime.MinValue.Ticks         ? new DateTime(DateTime.MinValue.Ticks, DateTimeKind.Utc)         : ticks > DateTime.MaxValue.Ticks         ? new DateTime(DateTime.MaxValue.Ticks, DateTimeKind.Utc)         : new DateTime(ticks, DateTimeKind.Utc); } Finally, we can describe our custom hopping window operator: public static IQWindowedStreamable<T> HoppingWindow2<T>(     IQStreamable<T> source,     TimeSpan duration,     TimeSpan interval,     DateTime alignment) {     if (source == null) { throw new ArgumentNullException("source"); }     return SnapToWindowIntervals(source, duration, interval, alignment).SnapshotWindow(); } By switching from HoppingWindow to HoppingWindow2 in the following example, the query returns quickly rather than gobbling resources and ultimately failing! public void Main() {     var start = new DateTimeOffset(new DateTime(2012, 6, 28), TimeSpan.Zero);     var duration = TimeSpan.FromSeconds(5);     var interval = TimeSpan.FromSeconds(2);     var alignment = new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc);     var events = this.Application.DefineEnumerable(() => new[]     {         EdgeEvent.CreateStart(start.AddSeconds(0), "e0"),         EdgeEvent.CreateStart(start.AddSeconds(1), "e1"),         EdgeEvent.CreateEnd(start.AddSeconds(1), start.AddSeconds(2), "e1"),         EdgeEvent.CreateStart(start.AddSeconds(3), "e2"),         EdgeEvent.CreateStart(start.AddSeconds(9), "e3"),         EdgeEvent.CreateEnd(start.AddSeconds(3), start.AddSeconds(10), "e2"),         EdgeEvent.CreateEnd(start.AddSeconds(9), start.AddSeconds(10), "e3"),     }).ToStreamable(AdvanceTimeSettings.IncreasingStartTime);     var adjustedEvents = SnapToWindowIntervals(events, duration, interval, alignment);     var query = from win in HoppingWindow2(events, duration, interval, alignment)                 select win.Count();     DisplayResults(adjustedEvents, "Adjusted Events");     DisplayResults(query, "Query"); } As you can see, instead of producing a massive number of windows for the open start edge e0, a single window is emitted from 12:00:15 AM until the end of time: Adjusted Events StartTime EndTime Payload 6/28/2012 12:00:01 AM 12/31/9999 11:59:59 PM e0 6/28/2012 12:00:03 AM 6/28/2012 12:00:07 AM e1 6/28/2012 12:00:05 AM 6/28/2012 12:00:15 AM e2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM e3 Query StartTime EndTime Payload 6/28/2012 12:00:01 AM 6/28/2012 12:00:03 AM 1 6/28/2012 12:00:03 AM 6/28/2012 12:00:05 AM 2 6/28/2012 12:00:05 AM 6/28/2012 12:00:07 AM 3 6/28/2012 12:00:07 AM 6/28/2012 12:00:11 AM 2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM 3 6/28/2012 12:00:15 AM 12/31/9999 11:59:59 PM 1 Regards, The StreamInsight Team

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• Building the Internet of Things – with Microsoft StreamInsight and the Microsoft .Net Micro Framework

  - by Roman Schindlauer

  Fresh from the press – The March 2012 issue of MSDN Magazine features an article about the Internet of Things. It discusses in depth how you can use StreamInsight to process all the data that is continuously produced in typical Internet of Things scenarios. It also gives you an end-to-end perspective on developing Internet of Things solutions in the .NET world, ranging from the .NET Micro Framework application running on the device, the communication between the devices and the server-side all the way to powerful cross-device streaming analytics implemented in StreamInsight LINQ. You can find an online version of the article here. Happy reading! Regards, The StreamInsight Team

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• Observable Adapter

  - by Roman Schindlauer

  .NET 4.0 introduced a pair of interfaces, IObservable<T> and IObserver<T>, supporting subscriptions to and notifications for push-based sequences. In combination with Reactive Extensions (Rx), these interfaces provide a convenient and uniform way of describing event sources and sinks in .NET. The StreamInsight CTP refresh in November 2009 included an Observable adapter supporting “reactive” event inputs and outputs.   While we continue to believe it enables an important programming model, the Observable adapter was not included in the final (RTM) release of Microsoft StreamInsight 1.0. The release takes a dependency on .NET 3.5 but for timing reasons could not take a dependency on .NET 4.0. Shipping a separate copy of the observable interfaces in StreamInsight – as we did in the CTP refresh – was not a viable option in the RTM release.   Within the next months, we will be shipping another preview of the Observable adapter that targets .NET 4.0. We look forward to gathering your feedback on the new adapter design! We plan to include the Observable adapter implementation into the product in a future release of Microsoft StreamInsight. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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• Changes to the LINQ-to-StreamInsight Dialect

  - by Roman Schindlauer

  In previous versions of StreamInsight (1.0 through 2.0), CepStream<> represents temporal streams of many varieties: Streams with ‘open’ inputs (e.g., those defined and composed over CepStream<T>.Create(string streamName) Streams with ‘partially bound’ inputs (e.g., those defined and composed over CepStream<T>.Create(Type adapterFactory, …)) Streams with fully bound inputs (e.g., those defined and composed over To*Stream – sequences or DQC) The stream may be embedded (where Server.Create is used) The stream may be remote (where Server.Connect is used) When adding support for new programming primitives in StreamInsight 2.1, we faced a choice: Add a fourth variety (use CepStream<> to represent streams that are bound the new programming model constructs), or introduce a separate type that represents temporal streams in the new user model. We opted for the latter. Introducing a new type has the effect of reducing the number of (confusing) runtime failures due to inappropriate uses of CepStream<> instances in the incorrect context. The new types are: IStreamable<>, which logically represents a temporal stream. IQStreamable<> : IStreamable<>, which represents a queryable temporal stream. Its relationship to IStreamable<> is analogous to the relationship of IQueryable<> to IEnumerable<>. The developer can compose temporal queries over remote stream sources using this type. The syntax of temporal queries composed over IQStreamable<> is mostly consistent with the syntax of our existing CepStream<>-based LINQ provider. However, we have taken the opportunity to refine certain aspects of the language surface. Differences are outlined below. Because 2.1 introduces new types to represent temporal queries, the changes outlined in this post do no impact existing StreamInsight applications using the existing types! SelectMany StreamInsight does not support the SelectMany operator in its usual form (which is analogous to SQL’s “CROSS APPLY” operator): static IEnumerable<R> SelectMany<T, R>(this IEnumerable<T> source, Func<T, IEnumerable<R>> collectionSelector) It instead uses SelectMany as a convenient syntactic representation of an inner join. The parameter to the selector function is thus unavailable. Because the parameter isn’t supported, its type in StreamInsight 1.0 – 2.0 wasn’t carefully scrutinized. Unfortunately, the type chosen for the parameter is nonsensical to LINQ programmers: static CepStream<R> SelectMany<T, R>(this CepStream<T> source, Expression<Func<CepStream<T>, CepStream<R>>> streamSelector) Using Unit as the type for the parameter accurately reflects the StreamInsight’s capabilities: static IQStreamable<R> SelectMany<T, R>(this IQStreamable<T> source, Expression<Func<Unit, IQStreamable<R>>> streamSelector) For queries that succeed – that is, queries that do not reference the stream selector parameter – there is no difference between the code written for the two overloads: from x in xs from y in ys select f(x, y) Top-K The Take operator used in StreamInsight causes confusion for LINQ programmers because it is applied to the (unbounded) stream rather than the (bounded) window, suggesting that the query as a whole will return k rows: (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) The use of SelectMany is also unfortunate in this context because it implies the availability of the window parameter within the remainder of the comprehension. The following compiles but fails at runtime: (from win in xs.SnapshotWindow() from x in win orderby x.A select win).Take(k) The Take operator in 2.1 is applied to the window rather than the stream: Before After (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) from win in xs.SnapshotWindow() from b in     (from x in win     orderby x.A     select x.B).Take(k) select b Multicast We are introducing an explicit multicast operator in order to preserve expression identity, which is important given the semantics about moving code to and from StreamInsight. This also better matches existing LINQ dialects, such as Reactive. This pattern enables expressing multicasting in two ways: Implicit Explicit var ys = from x in xs          where x.A > 1          select x; var zs = from y1 in ys          from y2 in ys.ShiftEventTime(_ => TimeSpan.FromSeconds(1))          select y1 + y2; var ys = from x in xs          where x.A > 1          select x; var zs = ys.Multicast(ys1 =>     from y1 in ys1     from y2 in ys1.ShiftEventTime(_ => TimeSpan.FromSeconds(1))     select y1 + y2; Notice the product translates an expression using implicit multicast into an expression using the explicit multicast operator. The user does not see this translation. Default window policies Only default window policies are supported in the new surface. Other policies can be simulated by using AlterEventLifetime. Before After xs.SnapshotWindow(     WindowInputPolicy.ClipToWindow,     SnapshotWindowInputPolicy.Clip) xs.SnapshotWindow() xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.PointAlignToWindowEnd) xs.TumblingWindow(     TimeSpan.FromSeconds(1)) xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.ClipToWindowEnd) Not supported … LeftAntiJoin Representation of LASJ as a correlated sub-query in the LINQ surface is problematic as the StreamInsight engine does not support correlated sub-queries (see discussion of SelectMany). The current syntax requires the introduction of an otherwise unsupported ‘IsEmpty()’ operator. As a result, the pattern is not discoverable and implies capabilities not present in the server. The direct representation of LASJ is used instead: Before After from x in xs where     (from y in ys     where x.A > y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, (x, y) => x.A > y.B) from x in xs where     (from y in ys     where x.A == y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, x => x.A, y => y.B) ApplyWithUnion The ApplyWithUnion methods have been deprecated since their signatures are redundant given the standard SelectMany overloads: Before After xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count()) xs.GroupBy(x => x.A).SelectMany(     gs =>     from win in gs.SnapshotWindow()     select win.Count()) xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count(), r => new { r.Key, Count = r.Payload }) from x in xs group x by x.A into gs from win in gs.SnapshotWindow() select new { gs.Key, Count = win.Count() } Alternate UDO syntax The representation of UDOs in the StreamInsight LINQ dialect confuses cardinalities. Based on the semantics of user-defined operators in StreamInsight, one would expect to construct queries in the following form: from win in xs.SnapshotWindow() from y in MyUdo(win) select y Instead, the UDO proxy method is referenced within a projection, and the (many) results returned by the user code are automatically flattened into a stream: from win in xs.SnapshotWindow() select MyUdo(win) The “many-or-one” confusion is exemplified by the following example that compiles but fails at runtime: from win in xs.SnapshotWindow() select MyUdo(win) + win.Count() The above query must fail because the UDO is in fact returning many values per window while the count aggregate is returning one. Original syntax New alternate syntax from win in xs.SnapshotWindow() select win.UdoProxy(1) from win in xs.SnapshotWindow() from y in win.UserDefinedOperator(() => new Udo(1)) select y -or- from win in xs.SnapshotWindow() from y in win.UdoMacro(1) select y Notice that this formulation also sidesteps the dynamic type pitfalls of the existing “proxy method” approach to UDOs, in which the type of the UDO implementation (TInput, TOuput) and the type of its constructor arguments (TConfig) need to align in a precise and non-obvious way with the argument and return types for the corresponding proxy method. UDSO syntax UDSO currently leverages the DataContractSerializer to clone initial state for logical instances of the user operator. Initial state will instead be described by an expression in the new LINQ surface. Before After xs.Scan(new Udso()) xs.Scan(() => new Udso()) Name changes ShiftEventTime => AlterEventStartTime: The alter event lifetime overload taking a new start time value has been renamed. CountByStartTimeWindow => CountWindow

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• What Is StreamInsight? A Primer for Non-Programmers

  - by Roman Schindlauer

  Are you trying to figure out whether StreamInsight might be something you could use, but you’re having trouble sifting through all the programming jargon that’s used to describe it? StreamInsight is, ultimately, a set of programming tools, and at some point it takes a programmer to implement a StreamInsight solution. But it really should be possible to get a handle on what StreamInsight is all about even if you’re not a programmer yourself. A new article published in the TechNet Wiki may be able to help: StreamInsight for Non-Programmers. It gives an overview of the technology, but it leaves out the C# references and relates StreamInsight to more familiar SQL databases and queries. Check it out. When you’re done there and are ready to dig a little deeper, take a look at Get Started with StreamInsight 2.1. That article should help you navigate through the StreamInsight official documentation and other resources. And, as always, you can post questions or comments here or on the TechNet Wiki. Regards, The StreamInsight Team

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• Windows in StreamInsight: Hopping vs. Snapshot

  - by Roman Schindlauer

  Three weeks ago, we explained the basic concept of windows in StreamInsight: defining sets of events that serve as arguments for set-based operations, like aggregations. Today, we want to discuss the so-called Hopping Windows and compare them with Snapshot Windows. We will compare these two, because they can serve similar purposes with different behaviors; we will discuss the remaining window type, Count Windows, another time. Hopping (and its syntactic-sugar-sister Tumbling) windows are probably the most straightforward windowing concept in StreamInsight. A hopping window is defined by its length, and the offset from one window to the next. They are aligned with some absolute point on the timeline (which can also be given as a parameter to the window) and create sets of events. The diagram below shows an example of a hopping window with length of 1h and hop size (the offset) of 15 minutes, hence creating overlapping windows:   Two aspects in this diagram are important: Since this window is overlapping, an event can fall into more than one windows. If an (interval) event spans a window boundary, its lifetime will be clipped to the window, before it is passed to the set-based operation. That’s the default and currently only available window input policy. (This should only concern you if you are using a time-sensitive user-defined aggregate or operator.) The set-based operation will be applied to each of these sets, yielding a result. This result is: A single scalar value in case of built-in or user-defined aggregates. A subset of the input payloads, in case of the TopK operator. Arbitrary events, when using a user-defined operator. The timestamps of the result are almost always the ones of the windows. Only the user-defined  operator can create new events with timestamps. (However, even these event lifetimes are subject to the window’s output policy, which is currently always to clip to the window end.) Let’s assume we were calculating the sum over some payload field: var result = from window in source.HoppingWindow( TimeSpan.FromHours(1), TimeSpan.FromMinutes(15), HoppingWindowOutputPolicy.ClipToWindowEnd) select new { avg = window.Avg(e => e.Value) }; Now each window is reflected by one result event:   As you can see, the window definition defines the output frequency. No matter how many or few events we got from the input, this hopping window will produce one result every 15 minutes – except for those windows that do not contain any events at all, because StreamInsight window operations are empty-preserving (more about that another time). The “forced” output for every window can become a performance issue if you have a real-time query with many events in a wide group & apply – let me explain: imagine you have a lot of events that you group by and then aggregate within each group – classical streaming pattern. The hopping window produces a result in each group at exactly the same point in time for all groups, since the window boundaries are aligned with the timeline, not with the event timestamps. This means that the query output will become very bursty, delivering the results of all the groups at the same point in time. This becomes especially obvious if the events are long-lasting, spanning multiple windows each, so that the produced result events do not change their value very often. In such a case, a snapshot window can remedy. Snapshot windows are more difficult to explain than hopping windows: they represent those periods in time, when no event changes occur. In other words, if you mark all event start and and times on your timeline, then you are looking at all snapshot window boundaries:   If your events are never overlapping, the snapshot window will not make much sense. It is commonly used together with timestamp modification, which make it a very powerful tool. Or as Allan Mitchell expressed in in a recent tweet: “I used to look at SnapshotWindow() with disdain. Now she is my mistress, the one I turn to in times of trouble and need”. Let’s look at a simple example: I want to compute the average of some value in my events over the last minute. I don’t want this output be produced at fixed intervals, but at soon as it changes (that’s the true event-driven spirit!). The snapshot window will include all currently active event at each point in time, hence we need to extend our original events’ lifetimes into the future: Applying the Snapshot window on these events, it will appear to be “looking back into the past”: If you look at the result produced in this diagram, you can easily prove that, at each point in time, the current event value represents the average of all original input event within the last minute. Here is the LINQ representation of that query, applying the lifetime extension before the snapshot window: var result = from window in source .AlterEventDuration(e => TimeSpan.FromMinutes(1)) .SnapshotWindow(SnapshotWindowOutputPolicy.Clip) select new { avg = window.Avg(e => e.Value) }; With more complex modifications of the event lifetimes you can achieve many more query patterns. For instance “running totals” by keeping the event start times, but snapping their end times to some fixed time, like the end of the day. Each snapshot then “sees” all events that have happened in the respective time period so far. Regards, The StreamInsight Team

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• What Is StreamInsight? A Primer for Non-Programmers

  - by Roman Schindlauer

  Are you trying to figure out whether StreamInsight might be something you could use, but you’re having trouble sifting through all the programming jargon that’s used to describe it? StreamInsight is, ultimately, a set of programming tools, and at some point it takes a programmer to implement a StreamInsight solution. But it really should be possible to get a handle on what StreamInsight is all about even if you’re not a programmer yourself. A new article published in the TechNet Wiki may be able to help: StreamInsight for Non-Programmers. It gives an overview of the technology, but it leaves out the C# references and relates StreamInsight to more familiar SQL databases and queries. Check it out. When you’re done there and are ready to dig a little deeper, take a look at Get Started with StreamInsight 2.1. That article should help you navigate through the StreamInsight official documentation and other resources. And, as always, you can post questions or comments here or on the TechNet Wiki. Regards, The StreamInsight Team

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• StreamInsight Now Available Through Microsoft Update

  - by Roman Schindlauer

  We are pleased to announce that StreamInsight v1.1 is now available for automatic download and install via Microsoft Update globally. In order to enable agile deployment of StreamInsight solutions, you have asked of us a steady cadence of releases with incremental, but highly impactful features and product improvements. Following our StreamInsight 1.0 launch in Spring 2010, we offered StreamInsight 1.1 in Fall 2010 with implicit compatibility and an upgraded setup to support side by side installs. With this setup, your applications will automatically point to the latest runtime, but you still have the choice to point your application back to a 1.0 runtime if you choose to do so. As the next step, in order to enable timely delivery of our releases to you, we are pleased to announce the support for automatic download and install of StreamInsight 1.1 release via Microsoft Update starting this week. If you have a computer: that is subscribed to Microsoft Update (different from Windows Update) has StreamInsight 1.0 installed, and does not yet have StreamInsight 1.1 installed, Microsoft Update will automatically download and install the corresponding StreamInsight 1.1 update side by side with your existing StreamInsight 1.0 installation – across all supported 32-bit and 64-bit Windows operating systems, across 11 supported languages, and across StreamInsight client and server SKUs. This is also supported in WSUS environments, if all your updates are managed from a corporate server (please talk to the WSUS administrator in your enterprise). As an example, if you have SI Client 1.0 DEU and SI Server 1.0 ENU installed on the same computer, Microsoft Update will selectively download and side-by-side install just the SI Client 1.1 DEU and SI Server 1.1 ENU releases. Going forward, Microsoft Update will be our preferred mode of delivery – in addition to support for our download sites, and media based distribution where appropriate. Regards, The StreamInsight Team

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• Observable Adapter

  - by Roman Schindlauer

  .NET 4.0 introduced a pair of interfaces, IObservable<T> and IObserver<T>, supporting subscriptions to and notifications for push-based sequences. In combination with Reactive Extensions (Rx), these interfaces provide a convenient and uniform way of describing event sources and sinks in .NET. The StreamInsight CTP refresh in November 2009 included an Observable adapter supporting “reactive” event inputs and outputs.   While we continue to believe it enables an important programming model, the Observable adapter was not included in the final (RTM) release of Microsoft StreamInsight 1.0. The release takes a dependency on .NET 3.5 but for timing reasons could not take a dependency on .NET 4.0. Shipping a separate copy of the observable interfaces in StreamInsight – as we did in the CTP refresh – was not a viable option in the RTM release.   Within the next months, we will be shipping another preview of the Observable adapter that targets .NET 4.0. We look forward to gathering your feedback on the new adapter design! We plan to include the Observable adapter implementation into the product in a future release of Microsoft StreamInsight. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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• StreamInsight 2.1, meet LINQ

  - by Roman Schindlauer

  Someone recently called LINQ “magic” in my hearing. I leapt to LINQ’s defense immediately. Turns out some people don’t realize “magic” is can be a pejorative term. I thought LINQ needed demystification. Here’s your best demystification resource: http://blogs.msdn.com/b/mattwar/archive/2008/11/18/linq-links.aspx. I won’t repeat much of what Matt Warren says in his excellent series, but will talk about some core ideas and how they affect the 2.1 release of StreamInsight. Let’s tell the story of a LINQ query. Compile time It begins with some code: IQueryable<Product> products = ...; var query = from p in products             where p.Name == "Widget"             select p.ProductID; foreach (int id in query) {     ... When the code is compiled, the C# compiler (among other things) de-sugars the query expression (see C# spec section 7.16): ... var query = products.Where(p => p.Name == "Widget").Select(p => p.ProductID); ... Overload resolution subsequently binds the Queryable.Where<Product> and Queryable.Select<Product, int> extension methods (see C# spec sections 7.5 and 7.6.5). After overload resolution, the compiler knows something interesting about the anonymous functions (lambda syntax) in the de-sugared code: they must be converted to expression trees, i.e.,“an object structure that represents the structure of the anonymous function itself” (see C# spec section 6.5). The conversion is equivalent to the following rewrite: ... var prm1 = Expression.Parameter(typeof(Product), "p"); var prm2 = Expression.Parameter(typeof(Product), "p"); var query = Queryable.Select<Product, int>(     Queryable.Where<Product>(         products,         Expression.Lambda<Func<Product, bool>>(Expression.Property(prm1, "Name"), prm1)),         Expression.Lambda<Func<Product, int>>(Expression.Property(prm2, "ProductID"), prm2)); ... If the “products” expression had type IEnumerable<Product>, the compiler would have chosen the Enumerable.Where and Enumerable.Select extension methods instead, in which case the anonymous functions would have been converted to delegates. At this point, we’ve reduced the LINQ query to familiar code that will compile in C# 2.0. (Note that I’m using C# snippets to illustrate transformations that occur in the compiler, not to suggest a viable compiler design!) Runtime When the above program is executed, the Queryable.Where method is invoked. It takes two arguments. The first is an IQueryable<> instance that exposes an Expression property and a Provider property. The second is an expression tree. The Queryable.Where method implementation looks something like this: public static IQueryable<T> Where<T>(this IQueryable<T> source, Expression<Func<T, bool>> predicate) {     return source.Provider.CreateQuery<T>(     Expression.Call(this method, source.Expression, Expression.Quote(predicate))); } Notice that the method is really just composing a new expression tree that calls itself with arguments derived from the source and predicate arguments. Also notice that the query object returned from the method is associated with the same provider as the source query. By invoking operator methods, we’re constructing an expression tree that describes a query. Interestingly, the compiler and operator methods are colluding to construct a query expression tree. The important takeaway is that expression trees are built in one of two ways: (1) by the compiler when it sees an anonymous function that needs to be converted to an expression tree, and; (2) by a query operator method that constructs a new queryable object with an expression tree rooted in a call to the operator method (self-referential). Next we hit the foreach block. At this point, the power of LINQ queries becomes apparent. The provider is able to determine how the query expression tree is evaluated! The code that began our story was intentionally vague about the definition of the “products” collection. Maybe it is a queryable in-memory collection of products: var products = new[]     { new Product { Name = "Widget", ProductID = 1 } }.AsQueryable(); The in-memory LINQ provider works by rewriting Queryable method calls to Enumerable method calls in the query expression tree. It then compiles the expression tree and evaluates it. It should be mentioned that the provider does not blindly rewrite all Queryable calls. It only rewrites a call when its arguments have been rewritten in a way that introduces a type mismatch, e.g. the first argument to Queryable.Where<Product> being rewritten as an expression of type IEnumerable<Product> from IQueryable<Product>. The type mismatch is triggered initially by a “leaf” expression like the one associated with the AsQueryable query: when the provider recognizes one of its own leaf expressions, it replaces the expression with the original IEnumerable<> constant expression. I like to think of this rewrite process as “type irritation” because the rewritten leaf expression is like a foreign body that triggers an immune response (further rewrites) in the tree. The technique ensures that only those portions of the expression tree constructed by a particular provider are rewritten by that provider: no type irritation, no rewrite. Let’s consider the behavior of an alternative LINQ provider. If “products” is a collection created by a LINQ to SQL provider: var products = new NorthwindDataContext().Products; the provider rewrites the expression tree as a SQL query that is then evaluated by your favorite RDBMS. The predicate may ultimately be evaluated using an index! In this example, the expression associated with the Products property is the “leaf” expression. StreamInsight 2.1 For the in-memory LINQ to Objects provider, a leaf is an in-memory collection. For LINQ to SQL, a leaf is a table or view. When defining a “process” in StreamInsight 2.1, what is a leaf? To StreamInsight a leaf is logic: an adapter, a sequence, or even a query targeting an entirely different LINQ provider! How do we represent the logic? Remember that a standing query may outlive the client that provisioned it. A reference to a sequence object in the client application is therefore not terribly useful. But if we instead represent the code constructing the sequence as an expression, we can host the sequence in the server: using (var server = Server.Connect(...)) {     var app = server.Applications["my application"];     var source = app.DefineObservable(() => Observable.Range(0, 10, Scheduler.NewThread));     var query = from i in source where i % 2 == 0 select i; } Example 1: defining a source and composing a query Let’s look in more detail at what’s happening in example 1. We first connect to the remote server and retrieve an existing app. Next, we define a simple Reactive sequence using the Observable.Range method. Notice that the call to the Range method is in the body of an anonymous function. This is important because it means the source sequence definition is in the form of an expression, rather than simply an opaque reference to an IObservable<int> object. The variation in Example 2 fails. Although it looks similar, the sequence is now a reference to an in-memory observable collection: var local = Observable.Range(0, 10, Scheduler.NewThread); var source = app.DefineObservable(() => local); // can’t serialize ‘local’! Example 2: error referencing unserializable local object The Define* methods support definitions of operator tree leaves that target the StreamInsight server. These methods all have the same basic structure. The definition argument is a lambda expression taking between 0 and 16 arguments and returning a source or sink. The method returns a proxy for the source or sink that can then be used for the usual style of LINQ query composition. The “define” methods exploit the compile-time C# feature that converts anonymous functions into translatable expression trees! Query composition exploits the runtime pattern that allows expression trees to be constructed by operators taking queryable and expression (Expression<>) arguments. The practical upshot: once you’ve Defined a source, you can compose LINQ queries in the familiar way using query expressions and operator combinators. Notably, queries can be composed using pull-sequences (LINQ to Objects IQueryable<> inputs), push sequences (Reactive IQbservable<> inputs), and temporal sequences (StreamInsight IQStreamable<> inputs). You can even construct processes that span these three domains using “bridge” method overloads (ToEnumerable, ToObservable and To*Streamable). Finally, the targeted rewrite via type irritation pattern is used to ensure that StreamInsight computations can leverage other LINQ providers as well. Consider the following example (this example depends on Interactive Extensions): var source = app.DefineEnumerable((int id) =>     EnumerableEx.Using(() =>         new NorthwindDataContext(), context =>             from p in context.Products             where p.ProductID == id             select p.ProductName)); Within the definition, StreamInsight has no reason to suspect that it ‘owns’ the Queryable.Where and Queryable.Select calls, and it can therefore defer to LINQ to SQL! Let’s use this source in the context of a StreamInsight process: var sink = app.DefineObserver(() => Observer.Create<string>(Console.WriteLine)); var query = from name in source(1).ToObservable()             where name == "Widget"             select name; using (query.Bind(sink).Run("process")) {     ... } When we run the binding, the source portion which filters on product ID and projects the product name is evaluated by SQL Server. Outside of the definition, responsibility for evaluation shifts to the StreamInsight server where we create a bridge to the Reactive Framework (using ToObservable) and evaluate an additional predicate. It’s incredibly easy to define computations that span multiple domains using these new features in StreamInsight 2.1! Regards, The StreamInsight Team

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• Taming Hopping Windows

  - by Roman Schindlauer

  At first glance, hopping windows seem fairly innocuous and obvious. They organize events into windows with a simple periodic definition: the windows have some duration d (e.g. a window covers 5 second time intervals), an interval or period p (e.g. a new window starts every 2 seconds) and an alignment a (e.g. one of those windows starts at 12:00 PM on March 15, 2012 UTC). var wins = xs     .HoppingWindow(TimeSpan.FromSeconds(5),                    TimeSpan.FromSeconds(2),                    new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc)); Logically, there is a window with start time a + np and end time a + np + d for every integer n. That’s a lot of windows. So why doesn’t the following query (always) blow up? var query = wins.Select(win => win.Count()); A few users have asked why StreamInsight doesn’t produce output for empty windows. Primarily it’s because there is an infinite number of empty windows! (Actually, StreamInsight uses DateTimeOffset.MaxValue to approximate “the end of time” and DateTimeOffset.MinValue to approximate “the beginning of time”, so the number of windows is lower in practice.) That was the good news. Now the bad news. Events also have duration. Consider the following simple input: var xs = this.Application                 .DefineEnumerable(() => new[]                     { EdgeEvent.CreateStart(DateTimeOffset.UtcNow, 0) })                 .ToStreamable(AdvanceTimeSettings.IncreasingStartTime); Because the event has no explicit end edge, it lasts until the end of time. So there are lots of non-empty windows if we apply a hopping window to that single event! For this reason, we need to be careful with hopping window queries in StreamInsight. Or we can switch to a custom implementation of hopping windows that doesn’t suffer from this shortcoming. The alternate window implementation produces output only when the input changes. We start by breaking up the timeline into non-overlapping intervals assigned to each window. In figure 1, six hopping windows (“Windows”) are assigned to six intervals (“Assignments”) in the timeline. Next we take input events (“Events”) and alter their lifetimes (“Altered Events”) so that they cover the intervals of the windows they intersect. In figure 1, you can see that the first event e1 intersects windows w1 and w2 so it is adjusted to cover assignments a1 and a2. Finally, we can use snapshot windows (“Snapshots”) to produce output for the hopping windows. Notice however that instead of having six windows generating output, we have only four. The first and second snapshots correspond to the first and second hopping windows. The remaining snapshots however cover two hopping windows each! While in this example we saved only two events, the savings can be more significant when the ratio of event duration to window duration is higher. Figure 1: Timeline The implementation of this strategy is straightforward. We need to set the start times of events to the start time of the interval assigned to the earliest window including the start time. Similarly, we need to modify the end times of events to the end time of the interval assigned to the latest window including the end time. The following snap-to-boundary function that rounds a timestamp value t down to the nearest value t' <= t such that t' is a + np for some integer n will be useful. For convenience, we will represent both DateTime and TimeSpan values using long ticks: static long SnapToBoundary(long t, long a, long p) {     return t - ((t - a) % p) - (t > a ? 0L : p); } How do we find the earliest window including the start time for an event? It’s the window following the last window that does not include the start time assuming that there are no gaps in the windows (i.e. duration < interval), and limitation of this solution. To find the end time of that antecedent window, we need to know the alignment of window ends: long e = a + (d % p); Using the window end alignment, we are finally ready to describe the start time selector: static long AdjustStartTime(long t, long e, long p) {     return SnapToBoundary(t, e, p) + p; } To find the latest window including the end time for an event, we look for the last window start time (non-inclusive): public static long AdjustEndTime(long t, long a, long d, long p) {     return SnapToBoundary(t - 1, a, p) + p + d; } Bringing it together, we can define the translation from events to ‘altered events’ as in Figure 1: public static IQStreamable<T> SnapToWindowIntervals<T>(IQStreamable<T> source, TimeSpan duration, TimeSpan interval, DateTime alignment) {     if (source == null) throw new ArgumentNullException("source");     // reason about DateTime and TimeSpan in ticks     long d = Math.Min(DateTime.MaxValue.Ticks, duration.Ticks);     long p = Math.Min(DateTime.MaxValue.Ticks, Math.Abs(interval.Ticks));     // set alignment to earliest possible window     var a = alignment.ToUniversalTime().Ticks % p;     // verify constraints of this solution     if (d <= 0L) { throw new ArgumentOutOfRangeException("duration"); }     if (p == 0L || p > d) { throw new ArgumentOutOfRangeException("interval"); }     // find the alignment of window ends     long e = a + (d % p);     return source.AlterEventLifetime(         evt => ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p)),         evt => ToDateTime(AdjustEndTime(evt.EndTime.ToUniversalTime().Ticks, a, d, p)) -             ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p))); } public static DateTime ToDateTime(long ticks) {     // just snap to min or max value rather than under/overflowing     return ticks < DateTime.MinValue.Ticks         ? new DateTime(DateTime.MinValue.Ticks, DateTimeKind.Utc)         : ticks > DateTime.MaxValue.Ticks         ? new DateTime(DateTime.MaxValue.Ticks, DateTimeKind.Utc)         : new DateTime(ticks, DateTimeKind.Utc); } Finally, we can describe our custom hopping window operator: public static IQWindowedStreamable<T> HoppingWindow2<T>(     IQStreamable<T> source,     TimeSpan duration,     TimeSpan interval,     DateTime alignment) {     if (source == null) { throw new ArgumentNullException("source"); }     return SnapToWindowIntervals(source, duration, interval, alignment).SnapshotWindow(); } By switching from HoppingWindow to HoppingWindow2 in the following example, the query returns quickly rather than gobbling resources and ultimately failing! public void Main() {     var start = new DateTimeOffset(new DateTime(2012, 6, 28), TimeSpan.Zero);     var duration = TimeSpan.FromSeconds(5);     var interval = TimeSpan.FromSeconds(2);     var alignment = new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc);     var events = this.Application.DefineEnumerable(() => new[]     {         EdgeEvent.CreateStart(start.AddSeconds(0), "e0"),         EdgeEvent.CreateStart(start.AddSeconds(1), "e1"),         EdgeEvent.CreateEnd(start.AddSeconds(1), start.AddSeconds(2), "e1"),         EdgeEvent.CreateStart(start.AddSeconds(3), "e2"),         EdgeEvent.CreateStart(start.AddSeconds(9), "e3"),         EdgeEvent.CreateEnd(start.AddSeconds(3), start.AddSeconds(10), "e2"),         EdgeEvent.CreateEnd(start.AddSeconds(9), start.AddSeconds(10), "e3"),     }).ToStreamable(AdvanceTimeSettings.IncreasingStartTime);     var adjustedEvents = SnapToWindowIntervals(events, duration, interval, alignment);     var query = from win in HoppingWindow2(events, duration, interval, alignment)                 select win.Count();     DisplayResults(adjustedEvents, "Adjusted Events");     DisplayResults(query, "Query"); } As you can see, instead of producing a massive number of windows for the open start edge e0, a single window is emitted from 12:00:15 AM until the end of time: Adjusted Events StartTime EndTime Payload 6/28/2012 12:00:01 AM 12/31/9999 11:59:59 PM e0 6/28/2012 12:00:03 AM 6/28/2012 12:00:07 AM e1 6/28/2012 12:00:05 AM 6/28/2012 12:00:15 AM e2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM e3 Query StartTime EndTime Payload 6/28/2012 12:00:01 AM 6/28/2012 12:00:03 AM 1 6/28/2012 12:00:03 AM 6/28/2012 12:00:05 AM 2 6/28/2012 12:00:05 AM 6/28/2012 12:00:07 AM 3 6/28/2012 12:00:07 AM 6/28/2012 12:00:11 AM 2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM 3 6/28/2012 12:00:15 AM 12/31/9999 11:59:59 PM 1 Regards, The StreamInsight Team

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• Changes to the LINQ-to-StreamInsight Dialect

  - by Roman Schindlauer

  In previous versions of StreamInsight (1.0 through 2.0), CepStream<> represents temporal streams of many varieties: Streams with ‘open’ inputs (e.g., those defined and composed over CepStream<T>.Create(string streamName) Streams with ‘partially bound’ inputs (e.g., those defined and composed over CepStream<T>.Create(Type adapterFactory, …)) Streams with fully bound inputs (e.g., those defined and composed over To*Stream – sequences or DQC) The stream may be embedded (where Server.Create is used) The stream may be remote (where Server.Connect is used) When adding support for new programming primitives in StreamInsight 2.1, we faced a choice: Add a fourth variety (use CepStream<> to represent streams that are bound the new programming model constructs), or introduce a separate type that represents temporal streams in the new user model. We opted for the latter. Introducing a new type has the effect of reducing the number of (confusing) runtime failures due to inappropriate uses of CepStream<> instances in the incorrect context. The new types are: IStreamable<>, which logically represents a temporal stream. IQStreamable<> : IStreamable<>, which represents a queryable temporal stream. Its relationship to IStreamable<> is analogous to the relationship of IQueryable<> to IEnumerable<>. The developer can compose temporal queries over remote stream sources using this type. The syntax of temporal queries composed over IQStreamable<> is mostly consistent with the syntax of our existing CepStream<>-based LINQ provider. However, we have taken the opportunity to refine certain aspects of the language surface. Differences are outlined below. Because 2.1 introduces new types to represent temporal queries, the changes outlined in this post do no impact existing StreamInsight applications using the existing types! SelectMany StreamInsight does not support the SelectMany operator in its usual form (which is analogous to SQL’s “CROSS APPLY” operator): static IEnumerable<R> SelectMany<T, R>(this IEnumerable<T> source, Func<T, IEnumerable<R>> collectionSelector) It instead uses SelectMany as a convenient syntactic representation of an inner join. The parameter to the selector function is thus unavailable. Because the parameter isn’t supported, its type in StreamInsight 1.0 – 2.0 wasn’t carefully scrutinized. Unfortunately, the type chosen for the parameter is nonsensical to LINQ programmers: static CepStream<R> SelectMany<T, R>(this CepStream<T> source, Expression<Func<CepStream<T>, CepStream<R>>> streamSelector) Using Unit as the type for the parameter accurately reflects the StreamInsight’s capabilities: static IQStreamable<R> SelectMany<T, R>(this IQStreamable<T> source, Expression<Func<Unit, IQStreamable<R>>> streamSelector) For queries that succeed – that is, queries that do not reference the stream selector parameter – there is no difference between the code written for the two overloads: from x in xs from y in ys select f(x, y) Top-K The Take operator used in StreamInsight causes confusion for LINQ programmers because it is applied to the (unbounded) stream rather than the (bounded) window, suggesting that the query as a whole will return k rows: (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) The use of SelectMany is also unfortunate in this context because it implies the availability of the window parameter within the remainder of the comprehension. The following compiles but fails at runtime: (from win in xs.SnapshotWindow() from x in win orderby x.A select win).Take(k) The Take operator in 2.1 is applied to the window rather than the stream: Before After (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) from win in xs.SnapshotWindow() from b in     (from x in win     orderby x.A     select x.B).Take(k) select b Multicast We are introducing an explicit multicast operator in order to preserve expression identity, which is important given the semantics about moving code to and from StreamInsight. This also better matches existing LINQ dialects, such as Reactive. This pattern enables expressing multicasting in two ways: Implicit Explicit var ys = from x in xs          where x.A > 1          select x; var zs = from y1 in ys          from y2 in ys.ShiftEventTime(_ => TimeSpan.FromSeconds(1))          select y1 + y2; var ys = from x in xs          where x.A > 1          select x; var zs = ys.Multicast(ys1 =>     from y1 in ys1     from y2 in ys1.ShiftEventTime(_ => TimeSpan.FromSeconds(1))     select y1 + y2; Notice the product translates an expression using implicit multicast into an expression using the explicit multicast operator. The user does not see this translation. Default window policies Only default window policies are supported in the new surface. Other policies can be simulated by using AlterEventLifetime. Before After xs.SnapshotWindow(     WindowInputPolicy.ClipToWindow,     SnapshotWindowInputPolicy.Clip) xs.SnapshotWindow() xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.PointAlignToWindowEnd) xs.TumblingWindow(     TimeSpan.FromSeconds(1)) xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.ClipToWindowEnd) Not supported … LeftAntiJoin Representation of LASJ as a correlated sub-query in the LINQ surface is problematic as the StreamInsight engine does not support correlated sub-queries (see discussion of SelectMany). The current syntax requires the introduction of an otherwise unsupported ‘IsEmpty()’ operator. As a result, the pattern is not discoverable and implies capabilities not present in the server. The direct representation of LASJ is used instead: Before After from x in xs where     (from y in ys     where x.A > y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, (x, y) => x.A > y.B) from x in xs where     (from y in ys     where x.A == y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, x => x.A, y => y.B) ApplyWithUnion The ApplyWithUnion methods have been deprecated since their signatures are redundant given the standard SelectMany overloads: Before After xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count()) xs.GroupBy(x => x.A).SelectMany(     gs =>     from win in gs.SnapshotWindow()     select win.Count()) xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count(), r => new { r.Key, Count = r.Payload }) from x in xs group x by x.A into gs from win in gs.SnapshotWindow() select new { gs.Key, Count = win.Count() } Alternate UDO syntax The representation of UDOs in the StreamInsight LINQ dialect confuses cardinalities. Based on the semantics of user-defined operators in StreamInsight, one would expect to construct queries in the following form: from win in xs.SnapshotWindow() from y in MyUdo(win) select y Instead, the UDO proxy method is referenced within a projection, and the (many) results returned by the user code are automatically flattened into a stream: from win in xs.SnapshotWindow() select MyUdo(win) The “many-or-one” confusion is exemplified by the following example that compiles but fails at runtime: from win in xs.SnapshotWindow() select MyUdo(win) + win.Count() The above query must fail because the UDO is in fact returning many values per window while the count aggregate is returning one. Original syntax New alternate syntax from win in xs.SnapshotWindow() select win.UdoProxy(1) from win in xs.SnapshotWindow() from y in win.UserDefinedOperator(() => new Udo(1)) select y -or- from win in xs.SnapshotWindow() from y in win.UdoMacro(1) select y Notice that this formulation also sidesteps the dynamic type pitfalls of the existing “proxy method” approach to UDOs, in which the type of the UDO implementation (TInput, TOuput) and the type of its constructor arguments (TConfig) need to align in a precise and non-obvious way with the argument and return types for the corresponding proxy method. UDSO syntax UDSO currently leverages the DataContractSerializer to clone initial state for logical instances of the user operator. Initial state will instead be described by an expression in the new LINQ surface. Before After xs.Scan(new Udso()) xs.Scan(() => new Udso()) Name changes ShiftEventTime => AlterEventStartTime: The alter event lifetime overload taking a new start time value has been renamed. CountByStartTimeWindow => CountWindow

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• Parameterized StreamInsight Queries

  - by Roman Schindlauer

  The changes in our APIs enable a set of scenarios that were either not possible before or could only be achieved through workarounds. One such use case that people ask about frequently is the ability to parameterize a query and instantiate it with different values instead of re-deploying the entire statement. I’ll demonstrate how to do this in StreamInsight 2.1 and combine it with a method of using subjects for dynamic query composition in a mini-series of (at least) two blog articles. Let’s start with something really simple: I want to deploy a windowed aggregate to a StreamInsight server, and later use it with different window sizes. The LINQ statement for such an aggregate is very straightforward and familiar: var result = from win in stream.TumblingWindow(TimeSpan.FromSeconds(5))               select win.Avg(e => e.Value); Obviously, we had to use an existing input stream object as well as a concrete TimeSpan value. If we want to be able to re-use this construct, we can define it as a IQStreamable: var avg = myApp     .DefineStreamable((IQStreamable<SourcePayload> s, TimeSpan w) =>         from win in s.TumblingWindow(w)         select win.Avg(e => e.Value)); The DefineStreamable API lets us define a function, in our case from a IQStreamable (the input stream) and a TimeSpan (the window length) to an IQStreamable (the result). We can then use it like a function, with the input stream and the window length as parameters: var result = avg(stream, TimeSpan.FromSeconds(5)); Nice, but you might ask: what does this save me, except from writing my own extension method? Well, in addition to defining the IQStreamable function, you can actually deploy it to the server, to make it re-usable by another process! When we deploy an artifact in V2.1, we give it a name: var avg = myApp     .DefineStreamable((IQStreamable<SourcePayload> s, TimeSpan w) =>         from win in s.TumblingWindow(w)         select win.Avg(e => e.Value))     .Deploy("AverageQuery"); When connected to the same server, we can now use that name to retrieve the IQStreamable and use it with our own parameters: var averageQuery = myApp     .GetStreamable<IQStreamable<SourcePayload>, TimeSpan, double>("AverageQuery"); var result = averageQuery(stream, TimeSpan.FromSeconds(5)); Convenient, isn’t it? Keep in mind that, even though the function “AverageQuery” is deployed to the server, its logic will still be instantiated into each process when the process is created. The advantage here is being able to deploy that function, so another client who wants to use it doesn’t need to ask the author for the code or assembly, but just needs to know the name of deployed entity. A few words on the function signature of GetStreamable: the last type parameter (here: double) is the payload type of the result, not the actual result stream’s type itself. The returned object is a function from IQStreamable<SourcePayload> and TimeSpan to IQStreamable<double>. In the next article we will integrate this usage of IQStreamables with Subjects in StreamInsight, so stay tuned! Regards, The StreamInsight Team

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• StreamInsight Now Available Through Microsoft Update

  - by Roman Schindlauer

  We are pleased to announce that StreamInsight v1.1 is now available for automatic download and install via Microsoft Update globally. In order to enable agile deployment of StreamInsight solutions, you have asked of us a steady cadence of releases with incremental, but highly impactful features and product improvements. Following our StreamInsight 1.0 launch in Spring 2010, we offered StreamInsight 1.1 in Fall 2010 with implicit compatibility and an upgraded setup to support side by side installs. With this setup, your applications will automatically point to the latest runtime, but you still have the choice to point your application back to a 1.0 runtime if you choose to do so. As the next step, in order to enable timely delivery of our releases to you, we are pleased to announce the support for automatic download and install of StreamInsight 1.1 release via Microsoft Update starting this week. If you have a computer: that is subscribed to Microsoft Update (different from Windows Update) has StreamInsight 1.0 installed, and does not yet have StreamInsight 1.1 installed, Microsoft Update will automatically download and install the corresponding StreamInsight 1.1 update side by side with your existing StreamInsight 1.0 installation – across all supported 32-bit and 64-bit Windows operating systems, across 11 supported languages, and across StreamInsight client and server SKUs. This is also supported in WSUS environments, if all your updates are managed from a corporate server (please talk to the WSUS administrator in your enterprise). As an example, if you have SI Client 1.0 DEU and SI Server 1.0 ENU installed on the same computer, Microsoft Update will selectively download and side-by-side install just the SI Client 1.1 DEU and SI Server 1.1 ENU releases. Going forward, Microsoft Update will be our preferred mode of delivery – in addition to support for our download sites, and media based distribution where appropriate. Regards, The StreamInsight Team

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• StreamInsight V2.0 Released!

  - by Roman Schindlauer

  The StreamInsight Team is proud to announce the release of StreamInsight V2.0! This is the version that ships with SQL 2012, and as such it has been available through Connect to SQL CTP customers already since December. As part of the SQL 2012 launch activities, we are now making V2.0 available to everyone, following our tradition of providing a separate download page. StreamInsight V2.0 includes a number of stability and performance fixes over its predecessor V1.2. Moreover it introduces a dependency on the .NET Framework 4.0, as well as on SQL 2012 license keys. For these reasons, we decided to bump the major version number, even though V2.0 does not add new features or API surface. It can be regarded a stepping stone to the upcoming release 2.1 which will contain significantly new APIs (that will depend on .NET 4.0). Head over here to download StreamInsight V2.0. The updated Books Online can be found here. Update: For instructions on how to make your existing application work against the new bits without recompilation, see here. Regards, The StreamInsight Team

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• StreamInsight will not push feature releases through Microsoft Update going forward

  - by Roman Schindlauer

  Until now, we've released StreamInsight through the Microsoft Download Center, and also released it out through Microsoft Update. Going forward, we will only release new StreamInsight versions through the Microsoft Download Center and only use MU to release service packs and security fixes (should any be needed). As a result of this decision, we are pulling off the recent StreamInsight 2.1 release from MU; this release is still available in Download Center. Don’t worry: there’s nothing wrong with the versions we’ve shipped in MU, we’ve just adjusted how we use MU. There is no action necessary from our customers as a result of this change, and we are not rolling back any changes to your current installation, so if you have installed StreamInsight 2.1 recently through the Microsoft Update, they will still work fine. Regards, The StreamInsight Team

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• StreamInsight 2.1 Released

  - by Roman Schindlauer

  The wait is over—we are pleased to announce the release of StreamInsight 2.1. Since the release of version 1.2, we have heard your feedbacks and suggestions and based on that we have come up with a whole new set of features. Here are some of the highlights: A New Programming Model – A more clear and consistent object model, eliminating the need for complex input and output adapters (though they are still completely supported). This new model allows you to provision, name, and manage data sources and sinks in the StreamInsight server. Tight integration with Reactive Framework (Rx) – You can write reactive queries hosted inside StreamInsight as well as compose temporal queries on reactive objects. High Availability – Check-pointing over temporal streams and multiple processes with shared computation. Here is how simple coding can be with the 2.1 Programming Model: class Program {     static void Main(string[] args)     {         using (Server server = Server.Create("Default"))         {             // Create an app             Application app = server.CreateApplication("app");             // Define a simple observable which generates an integer every second             var source = app.DefineObservable(() =>                 Observable.Interval(TimeSpan.FromSeconds(1)));             // Define a sink.             var sink = app.DefineObserver(() =>                 Observer.Create<long>(x => Console.WriteLine(x)));             // Define a query to filter the events             var query = from e in source                         where e % 2 == 0                         select e;             // Bind the query to the sink and create a runnable process             using (IDisposable proc = query.Bind(sink).Run("MyProcess"))             {                 Console.WriteLine("Press a key to dispose the process...");                 Console.ReadKey();             }         }     } }   That’s how easily you can define a source, sink and compose a query and run it. Note that we did not replace the existing APIs, they co-exist with the new surface. Stay tuned, you will see a series of articles coming out over the next few weeks about the new features and how to use them. Come and grab it from our download center page and let us know what you think! You can find the updated MSDN documentation here, and we would appreciate if you could provide feedback to the docs as well—best via email to [email protected]. Moreover, we updated our samples to demonstrate the new programming surface. Regards, The StreamInsight Team

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• StreamInsight 2.1 Released

  - by Roman Schindlauer

  The wait is over—we are pleased to announce the release of StreamInsight 2.1. Since the release of version 1.2, we have heard your feedbacks and suggestions and based on that we have come up with a whole new set of features. Here are some of the highlights: A New Programming Model – A more clear and consistent object model, eliminating the need for complex input and output adapters (though they are still completely supported). This new model allows you to provision, name, and manage data sources and sinks in the StreamInsight server. Tight integration with Reactive Framework (Rx) – You can write reactive queries hosted inside StreamInsight as well as compose temporal queries on reactive objects. High Availability – Check-pointing over temporal streams and multiple processes with shared computation. Here is how simple coding can be with the 2.1 Programming Model: class Program {     static void Main(string[] args)     {         using (Server server = Server.Create("Default"))         {             // Create an app             Application app = server.CreateApplication("app");             // Define a simple observable which generates an integer every second             var source = app.DefineObservable(() =>                 Observable.Interval(TimeSpan.FromSeconds(1)));             // Define a sink.             var sink = app.DefineObserver(() =>                 Observer.Create<long>(x => Console.WriteLine(x)));             // Define a query to filter the events             var query = from e in source                         where e % 2 == 0                         select e;             // Bind the query to the sink and create a runnable process             using (IDisposable proc = query.Bind(sink).Run("MyProcess"))             {                 Console.WriteLine("Press a key to dispose the process...");                 Console.ReadKey();             }         }     } }   That’s how easily you can define a source, sink and compose a query and run it. Note that we did not replace the existing APIs, they co-exist with the new surface. Stay tuned, you will see a series of articles coming out over the next few weeks about the new features and how to use them. Come and grab it from our download center page and let us know what you think! You can find the updated MSDN documentation here, and we would appreciate if you could provide feedback to the docs as well—best via email to [email protected]. Moreover, we updated our samples to demonstrate the new programming surface. Regards, The StreamInsight Team

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• StreamInsight V2.0 Released!

  - by Roman Schindlauer

  The StreamInsight Team is proud to announce the release of StreamInsight V2.0! This is the version that ships with SQL 2012, and as such it has been available through Connect to SQL CTP customers already since December. As part of the SQL 2012 launch activities, we are now making V2.0 available to everyone, following our tradition of providing a separate download page. StreamInsight V2.0 includes a number of stability and performance fixes over its predecessor V1.2. Moreover it introduces a dependency on the .NET Framework 4.0, as well as on SQL 2012 license keys. For these reasons, we decided to bump the major version number, even though V2.0 does not add new features or API surface. It can be regarded a stepping stone to the upcoming release 2.1 which will contain significantly new APIs (that will depend on .NET 4.0). Head over here to download StreamInsight V2.0. The updated Books Online can be found here. Update: For instructions on how to make your existing application work against the new bits without recompilation, see here. Regards, The StreamInsight Team

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• Using Subjects to Deploy Queries Dynamically

  - by Roman Schindlauer

  In the previous blog posting, we showed how to construct and deploy query fragments to a StreamInsight server, and how to re-use them later. In today’s posting we’ll integrate this pattern into a method of dynamically composing a new query with an existing one. The construct that enables this scenario in StreamInsight V2.1 is a Subject. A Subject lets me create a junction element in an existing query that I can tap into while the query is running. To set this up as an end-to-end example, let’s first define a stream simulator as our data source: var generator = myApp.DefineObservable(     (TimeSpan t) => Observable.Interval(t).Select(_ => new SourcePayload())); This ‘generator’ produces a new instance of SourcePayload with a period of t (system time) as an IObservable. SourcePayload happens to have a property of type double as its payload data. Let’s also define a sink for our example—an IObserver of double values that writes to the console: var console = myApp.DefineObserver(     (string label) => Observer.Create<double>(e => Console.WriteLine("{0}: {1}", label, e)))     .Deploy("ConsoleSink"); The observer takes a string as parameter which is used as a label on the console, so that we can distinguish the output of different sink instances. Note that we also deploy this observer, so that we can retrieve it later from the server from a different process. Remember how we defined the aggregation as an IQStreamable function in the previous article? We will use that as well: var avg = myApp     .DefineStreamable((IQStreamable<SourcePayload> s, TimeSpan w) =>         from win in s.TumblingWindow(w)         select win.Avg(e => e.Value))     .Deploy("AverageQuery"); Then we define the Subject, which acts as an observable sequence as well as an observer. Thus, we can feed a single source into the Subject and have multiple consumers—that can come and go at runtime—on the other side: var subject = myApp.CreateSubject("Subject", () => new Subject<SourcePayload>()); Subject are always deployed automatically. Their name is used to retrieve them from a (potentially) different process (see below). Note that the Subject as we defined it here doesn’t know anything about temporal streams. It is merely a sequence of SourcePayloads, without any notion of StreamInsight point events or CTIs. So in order to compose a temporal query on top of the Subject, we need to 'promote' the sequence of SourcePayloads into an IQStreamable of point events, including CTIs: var stream = subject.ToPointStreamable(     e => PointEvent.CreateInsert<SourcePayload>(e.Timestamp, e),     AdvanceTimeSettings.StrictlyIncreasingStartTime); In a later posting we will show how to use Subjects that have more awareness of time and can be used as a junction between QStreamables instead of IQbservables. Having turned the Subject into a temporal stream, we can now define the aggregate on this stream. We will use the IQStreamable entity avg that we defined above: var longAverages = avg(stream, TimeSpan.FromSeconds(5)); In order to run the query, we need to bind it to a sink, and bind the subject to the source: var standardQuery = longAverages     .Bind(console("5sec average"))     .With(generator(TimeSpan.FromMilliseconds(300)).Bind(subject)); Lastly, we start the process: standardQuery.Run("StandardProcess"); Now we have a simple query running end-to-end, producing results. What follows next is the crucial part of tapping into the Subject and adding another query that runs in parallel, using the same query definition (the “AverageQuery”) but with a different window length. We are assuming that we connected to the same StreamInsight server from a different process or even client, and thus have to retrieve the previously deployed entities through their names: // simulate the addition of a 'fast' query from a separate server connection, // by retrieving the aggregation query fragment // (instead of simply using the 'avg' object) var averageQuery = myApp     .GetStreamable<IQStreamable<SourcePayload>, TimeSpan, double>("AverageQuery"); // retrieve the input sequence as a subject var inputSequence = myApp     .GetSubject<SourcePayload, SourcePayload>("Subject"); // retrieve the registered sink var sink = myApp.GetObserver<string, double>("ConsoleSink"); // turn the sequence into a temporal stream var stream2 = inputSequence.ToPointStreamable(     e => PointEvent.CreateInsert<SourcePayload>(e.Timestamp, e),     AdvanceTimeSettings.StrictlyIncreasingStartTime); // apply the query, now with a different window length var shortAverages = averageQuery(stream2, TimeSpan.FromSeconds(1)); // bind new sink to query and run it var fastQuery = shortAverages     .Bind(sink("1sec average"))     .Run("FastProcess"); The attached solution demonstrates the sample end-to-end. Regards, The StreamInsight Team

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• ¿Es más barato desarrollar a medida que adquirir un ERP?

  - by Luis Alberto Quilez

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} La clave está en el tiempo. Cuando abordamos un desarrollo a medida, estamos pensando únicamente en las necesidades de hoy. Tenemos un proyecto concreto, un determinado alcance funcional y conocemos las herramientas que hoy tenemos disponibles. Somos los que mejor conocemos nuestra empresa de hoy, sus procesos y el desarrollo parece una buena opción, pues las licencias de las herramientas de desarrollo son económicas y el coste de la tarifa diaria de programación es asequible, y entonces, caemos en la trampa del corto plazo y vamos adelante. Es muy posible que este desarrollo salga bien, que estemos orgullosos de nuestro trabajo, e incluso que proclamemos a los 4 vientos el dinero que nos hemos ahorrado. Sin embargo el mundo no se para, el negocio no se para, la adaptación debe ser permanente, nuestros clientes, internos y externos, tendrán nuevas exigencias y nuestro desarrollo no estará terminado, tendremos que integrarlo con otras áreas, tendremos que tratar de darle mayor funcionalidad y alcance, tendremos que adaptarlo a las nuevas tecnologías, permitir que la información se analice, se comparta, se acceda desde nuevos dispositivos … y veremos en primera persona cómo la trampa del desarrollo se cierra sobre nuestras cabezas, nunca estará terminado, la tecnología que usamos un día se quedará obsoleta, el ritmo de exigencia por funcionalidad e integración será cada vez mayor y no podremos sino poner más y más recursos dedicados al mantenimiento de un desarrollo propio, que no deja de comer, que me obliga a gastar más y más cada día y del que no puedo salir. Al poco tiempo me he convertido en una empresa de desarrollo de software dentro de mi propia empresa y ni tengo los recursos económicos para hacerlo viable, ni tengo las capacidades humanas y de inversión para responder a lo que se me exige desde el negocio. Así que pensemos, desde el principio, en que nuestra empresa debe perdurar muchos años, y hagamos el análisis de costes bajo esta perspectiva a la hora de tomar la decisión y veremos entonces que la adquisición de un ERP es mucho más económica que el desarrollo a medida. Por otro lado tenemos la integración. Un sistema de producción, requiere la asignación de recursos, que a su vez requieren de un plan de desarrollo, una formación o un cálculo de su nómina; también requiere de una cuenta contable, de una gestión de compras o de una asignación de costes y claro,de todos estos puntos nos vamos dando cuenta sobre la marcha, cuando en un sistema de gestión integral (ERP) lo tenemos disponible desde el primer momento. Claro que no nos vale un ERP cerrado, poco flexible y que no me permita diferenciar a mi empresa. Tenemos que buscar un socio tecnológico que nos acompañe, que asuma la inversión en tecnología y que me vaya suministrando versiones y soluciones acordes a las exigencias de los tiempos, de hoy y de mañana, pero además que me permita adaptar los flujos e innovar en los procesos para que podamos diferenciar nuestra empresa de la competencia, hoy y mañana. Veremos cómo, con la decisión de un ERP, flexible y abierto, los números salen y en el largo plazo es mucho más económica la decisión de adquirir un ERP que de optar por el desarrollo. Luis Alberto Quilez v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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• Oracle at Gartner IAM Summit Next Week

  - by Tanu Sood

  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} Heading to Gartner Identity and Access Management Summit next week? As you know, one of the premier conferences for identity management specialists and security experts, the Gartner IAM Conference this year is in Las Vegas, Nevada from December 3 – 5. Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";} As you pack your bags and plan your itinerary, do note that Oracle executives including Amit Jasuja, Senior Vice President, Security and Identity Management and Dave Profozich, Group Vice President along with product management and implementation experts would be in attendance. You are invited to meet with the Oracle team and mingle with our customers. We recommend you bookmark the following times and activities: Breakfast Keynote: Trends in Identity Management Tuesday, December 4, 2012 7:30 a.m. – 8:00 a.m., Octavius 16 Amit Jasuja, SVP, Security and Identity Management, Oracle Ranjan Jain, Enterprise Architect, Cisco Don’t miss the opportunity to hear from Amit Jasuja, SVP, Security and Identity Management as he discusses how mobile and social behavior are changing how organizations function, manage their workforce, and interact with their customers. Learn how these new trends are shaping the innovations in Oracle Identity Management solutions. And get a customer’s take on the new trends and their impact on the organization. Visit the Oracle Booth Mingle with peers, customers, product and implementation experts at the Oracle booth. While there, catch live demonstrations of the very latest best-in-class technologies and learn how Oracle Identity Management solutions are enabling the Social, Mobile and Cloud (SoMoClo) environments. And arm yourself with industry resources from our Virtual Collateral Rack. And don’t forget to enter for a chance to win a JAWBONE JAMBOX Wireless Speaker System while at our booth. So, see you there? Gartner Identity and Access Management Summit December 3 -5, 2012 Caesars Palace 3570 Las Vegas Blvd South Las Vegas, NV 89109

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• Let’s Get Social

  - by Kristin Rose

  v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} You can try to run from it like a bad Facebook picture but you can’t hide. Social media as we know it is quickly taking over our lives and is not going away any time soon. Though attempting to reach as many Twitter followers as Lady Gaga is daunting, learning how to leverage social media to meet your customer’s needs is not. For Oracle, this means interacting directly with our partners through our many social media outlets, and refraining from posting a mindless status on the pastrami on rye we ate for lunch today… though it was delicious. The “correct” way to go about social media is going to mean something different to each company. For example, sending a customer more than one friend request a day may not be the best way to get their attention, but using social media as a two-way marketing channel is. Oracle’s Partner Business Center’s (PBC) twitter handle was recently mentioned by Elateral as the “ideal way to engage with your market and use social media in the channel”. Why you ask? Because the PBC has two named social media leads manning the Twitter feed at all times, helping partners get the information and answers they need more quickly than a Justin Bieber video gone viral. So whether you want to post a video of your favorite customer attempting the Marshmallow challenge or tweet like there’s no tomorrow, be sure to follow @OraclePartnerBiz today, and see how they can help you achieve your next partner milestone with Oracle. Happy Socializing, The OPN Communications Team v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Normal 0 false false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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• La Universidad Estatal de California estandariza 23 campus a través de Oracle PeopleSoft

  - by Noelia Gomez

  Normal 0 21 false false false ES X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Calibri","sans-serif"; mso-ascii- mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi- mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} El sistema universitario más grande de los Estados Unidos consolida la Gestión Financiera y consolidará la gestión del capital humano para mejorar la eficiencia y reducir los costes. Normal 0 21 false false false ES X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Calibri","sans-serif"; mso-ascii- mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi- mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} La Universidad Estatal de California (CSU) está estandarizando su sistema de 23 campus y la Oficina del Rector con las aplicaciones de Oracle PeopleSoft. La CSU es el mayor sistema universitario público en los Estados Unidos. Los premios CSU con 90.000 grados por año y desde su creación en 1961, ha otorgado casi 2,6 millones. Como parte de su plan estratégico, “Acces to Excellence”, la CSU se ha comprometido a tomar ventaja de la tecnología para satisfacer las futuras necesidades de educación y ha creado los Sistemas Comunes de Gestión (CMS). La misión de CMS es brindar un servicio de calidad eficiente, eficaz y de calidad a los estudiantes, profesores y personal de los 23 campus de la CSU y la Oficina del Decanato. En un esfuerzo por mejorar la eficiencia y reducir los costes de todo el sistema, la CSU ha consolidado sus procesos de gestión financiera y los sistemas a través de PeopleSoft Financial Management. Para proporcionar una visión unificada de las operaciones financieras, CSU ha consolidado 22 campus en un mismo sistema a través de PeopleSoft Financials. Estas aplicaciones incluyen: Contabilidad, Facturación, Cuentas a Pagar, Cuentas a Cobrar y Compras. CSU también utiliza Oracle Business Intelligence Enterprise Edition para el análisis y presentación de informes. CSU está consolidando en PeopleSoft Human Capital Management (HCM) en pos de varios objetivos estratégicos como, entre otros: atraer y retener al personal superior y de la facultad. El sistema financiero consolidad de la CSU es ahora el mayor despliegue único de educación superior de PeopleSoft Financials en los Estados Unidos. Una centralización de esta envergadura no tiene precedentes, y sin embargo todavía se completó a tiempo y dentro del presupuesto. Una vez completado, la CSU también será el mayor despliegue de educación superior de PeopleSoft HCM. CSU también utiliza PeopleSoft Campus Solutions para gestionar sus operaciones académicas de los estudiantes, incluyendo la programación y registro de clases, cálculo y recaudación de las tasas, la concesión de la ayuda financiera y la evaluación y el progreso académico de los estudiantes. Las aplicaciones de Oracle PeopleSoft están diseñadas para atender las necesidades de negocio más complejas. Estas proveen soluciones integrales de negocios y de industria, permitiendo a las organizaciones aumentar la productividad, acelerar el rendimiento del negocio y un menor coste total de propiedad.PeopleSoft de Oracle Campus Solutions es una completa suite de software diseñado para las necesidades cambiantes de las instituciones de educación superior. Oracle sigue colaborando con la CSU y los colegios y universidades de todo el mundo para ofrecer los sistemas de administración y desarrollo de estudiantes más sensible y comprensivo y disponibles en la actualidad. Unisys está implantando aplicaciones PeopleSoft CSU y facilitando el acceso a los estudiantes, profesores y personal de las universidades a través de la solución de Unisys Hosted Secure Private Cloud. Unisys es un miembro de nivel Platino de Oracle PartnerNetwork (OPN). "Con el fin de seguir cumpliendo los objetivos de nuestro plan estratégico, creemos que es fundamental para estandarizar nuestros procesos operativos - tanto en el back office como allí donde lleguen nuestros estudiantes todos los días - maximizar nuestra inversión en tecnología y reducir costes", dijo Larry Furukawa-Schlereth, Director de Finanzas, Sonoma State University, y presidente del Comité Global Ejecutivo de Gestión de la CSU, California State University. "Contar con una vista única de toda la información importante en un sistema ayuda a la Universidad Estatal de California a continuar ofreciendo excelentes oportunidades de educación a un coste asequible mientras ayudamos a dar forma a la futura calidad de vida cívica y económica en California". Conozca más sobre Peoplesoft aquí: Normal 0 21 false false false ES X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Calibri","sans-serif"; mso-ascii- mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi- mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Oracle’s PeopleSoft Applications Oracle in Higher Education Oracle’s PeopleSoft Financial Management Oracle’s PeopleSoft Human Capital Management Oracle’s PeopleSoft Campus Solutions Oracle Human Capital Management Blog Oracle HCM on Twitter Oracle Higher Education on Facebook Oracle Higher Education on Diigo

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