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  • Big Oh Notation - formal definition.

    - by aloh
    I'm reading a textbook right now for my Java III class. We're reading about Big-Oh and I'm a little confused by its formal definition. Formal Definition: "A function f(n) is of order at most g(n) - that is, f(n) = O(g(n)) - if a positive real number c and positive integer N exist such that f(n) <= c g(n) for all n = N. That is, c g(n) is an upper bound on f(n) when n is sufficiently large." Ok, that makes sense. But hold on, keep reading...the book gave me this example: "In segment 9.14, we said that an algorithm that uses 5n + 3 operations is O(n). We now can show that 5n + 3 = O(n) by using the formal definition of Big Oh. When n = 3, 5n + 3 <= 5n + n = 6n. Thus, if we let f(n) = 5n + 3, g(n) = n, c = 6, N = 3, we have shown that f(n) <= 6 g(n) for n = 3, or 5n + 3 = O(n). That is, if an algorithm requires time directly proportional to 5n + 3, it is O(n)." Ok, this kind of makes sense to me. They're saying that if n = 3 or greater, 5n + 3 takes less time than if n was less than 3 - thus 5n + n = 6n - right? Makes sense, since if n was 2, 5n + 3 = 13 while 6n = 12 but when n is 3 or greater 5n + 3 will always be less than or equal to 6n. Here's where I get confused. They give me another example: Example 2: "Let's show that 4n^2 + 50n - 10 = O(n^2). It is easy to see that: 4n^2 + 50n - 10 <= 4n^2 + 50n for any n. Since 50n <= 50n^2 for n = 50, 4n^2 + 50n - 10 <= 4n^2 + 50n^2 = 54n^2 for n = 50. Thus, with c = 54 and N = 50, we have shown that 4n^2 + 50n - 10 = O(n^2)." This statement doesn't make sense: 50n <= 50n^2 for n = 50. Isn't any n going to make the 50n less than 50n^2? Not just greater than or equal to 50? Why did they even mention that 50n <= 50n^2? What does that have to do with the problem? Also, 4n^2 + 50n - 10 <= 4n^2 + 50n^2 = 54n^2 for n = 50 is going to be true no matter what n is. And how in the world does picking numbers show that f(n) = O(g(n))? Please help me understand! :(

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  • Big O Complexity of a method

    - by timeNomad
    I have this method: public static int what(String str, char start, char end) { int count=0; for(int i=0;i<str.length(); i++) { if(str.charAt(i) == start) { for(int j=i+1;j<str.length(); j++) { if(str.charAt(j) == end) count++; } } } return count; } What I need to find is: 1) What is it doing? Answer: counting the total number of end occurrences after EACH (or is it? Not specified in the assignment, point 3 depends on this) start. 2) What is its complexity? Answer: the first loops iterates over the string completely, so it's at least O(n), the second loop executes only if start char is found and even then partially (index at which start was found + 1). Although, big O is all about worst case no? So in the worst case, start is the 1st char & the inner iteration iterates over the string n-1 times, the -1 is a constant so it's n. But, the inner loop won't be executed every outer iteration pass, statistically, but since big O is about worst case, is it correct to say the complexity of it is O(n^2)? Ignoring any constants and the fact that in 99.99% of times the inner loop won't execute every outer loop pass. 3) Rewrite it so that complexity is lower. What I'm not sure of is whether start occurs at most once or more, if once at most, then method can be rewritten using one loop (having a flag indicating whether start has been encountered and from there on incrementing count at each end occurrence), yielding a complexity of O(n). In case though, that start can appear multiple times, which most likely it is, because assignment is of a Java course and I don't think they would make such ambiguity. Solving, in this case, is not possible using one loop... WAIT! Yes it is..! Just have a variable, say, inc to be incremented each time start is encountered & used to increment count each time end is encountered after the 1st start was found: inc = 0, count = 0 if (current char == start) inc++ if (inc > 0 && current char == end) count += inc This would also yield a complexity of O(n)? Because there is only 1 loop. Yes I realize I wrote a lot hehe, but what I also realized is that I understand a lot better by forming my thoughts into words...

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  • big O notation algorithm

    - by niggersak
    Use big-O notation to classify the traditional grade school algorithms for addition and multiplication. That is, if asked to add two numbers each having N digits, how many individual additions must be performed? If asked to multiply two N-digit numbers, how many individual multiplications are required? . Suppose f is a function that returns the result of reversing the string of symbols given as its input, and g is a function that returns the concatenation of the two strings given as its input. If x is the string hrwa, what is returned by g(f(x),x)? Explain your answer - don't just provide the result!

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  • Tricky Big-O complexity

    - by timeNomad
    public void foo (int n, int m) { int i = m; while (i > 100) i = i/3; for (int k=i ; k>=0; k--) { for (int j=1; j<n; j*=2) System.out.print(k + "\t" + j); System.out.println(); } } I figured the complexity would be O(logn). That is as a product of the inner loop, the outer loop -- will never be executed more than 100 times, so it can be omitted. What I'm not sure about is the while clause, should it be incorporated into the Big-O complexity? For very large i values it could make an impact, or arithmetic operations, doesn't matter on what scale, count as basic operations and can be omitted?

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  • Database indexes and their Big-O notation

    - by miket2e
    I'm trying to understand the performance of database indexes in terms of Big-O notation. Without knowing much about it, I would guess that: Querying on a primary key or unique index will give you a O(1) lookup time. Querying on a non-unique index will also give a O(1) time, albeit maybe the '1' is slower than for the unique index (?) Querying on a column without an index will give a O(N) lookup time (full table scan). Is this generally correct ? Will querying on a primary key ever give worse performance than O(1) ? My specific concern is for SQLite, but I'd be interested in knowing to what extent this varies between different databases too.

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  • Can someone help with big O notation?

    - by Dann
    void printScientificNotation(double value, int powerOfTen) { if (value >= 1.0 && value < 10.0) { System.out.println(value + " x 10^" + powerOfTen); } else if (value < 1.0) { printScientificNotation(value * 10, powerOfTen - 1); } else // value >= 10.0 { printScientificNotation(value / 10, powerOfTen + 1); } } I understand how the method goes but I cannot figure out a way to represent the method. For example, if value was 0.00000009 or 9e-8, the method will call on printScientificNotation(value * 10, powerOfTen - 1); eight times and System.out.println(value + " x 10^" + powerOfTen); once. So the it is called recursively by the exponent for e. But how do I represent this by big O notation? Thanks!

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  • Book Review (Book 10) - The Information: A History, a Theory, a Flood

    - by BuckWoody
    This is a continuation of the books I challenged myself to read to help my career - one a month, for year. You can read my first book review here, and the entire list is here. The book I chose for March 2012 was: The Information: A History, a Theory, a Flood by James Gleick. I was traveling at the end of last month so I’m a bit late posting this review here. Why I chose this book: My personal belief about computing is this: All computing technology is simply re-arranging data. We take data in, we manipulate it, and we send it back out. That’s computing. I had heard from some folks about this book and it’s treatment of data. I heard that it dealt with the basics of data - and the semantics of data, information and so on. It also deals with the earliest forms of history of information, which fascinates me. It’s similar I was told, to GEB which a favorite book of mine as well, so that was a bonus. Some folks I talked to liked it, some didn’t - so I thought I would check it out. What I learned: I liked the book. It was longer than I thought - took quite a while to read, even though I tend to read quickly. This is the kind of book you take your time with. It does in fact deal with the earliest forms of human interaction and the basics of data. I learned, for instance, that the genesis of the binary communication system is based in the invention of telegraph (far-writing) codes, and that the earliest forms of communication were expensive. In fact, many ciphers were invented not to hide military secrets, but to compress information. A sort of early “lol-speak” to keep the cost of transmitting data low! I think the comparison with GEB is a bit over-reaching. GEB is far more specific, fanciful and so on. In fact, this book felt more like something fro Richard Dawkins, and tended to wander around the subject quite a bit. I imagine the author doing his research and writing each chapter as a book that followed on from the last one. This is what possibly bothered those who tended not to like it, I think. Towards the middle of the book, I think the author tended to be a bit too fragmented even for me. He began to delve into memes, biology and more - I think he might have been better off breaking that off into another work. The existentialism just seemed jarring. All in all, I liked the book. I recommend it to any technical professional, specifically ones involved with data technology in specific. And isn’t that all of us? :)

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  • Prove that the set of regular languages is a proper subset of the set of the context-free languages

    - by David Relihan
    I was brushing up (not homework)on some computation-theory and came accross this problem: How can you prove that the set of regular languages is a proper subset of the set of the context-free languages. Now I know a language is regular iff it is accepted by a finite automaton. And I know a language is context-free iff it is accepted by a pushdown automaton. But I'm not sure of what solution is.

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  • Big Data for Retail

    - by David Dorf
    Right up there with mobile, social, and cloud is the term "big data," which seems to be popping up lots in the press these days.  Companies like Google, Yahoo, and Facebook have popularized a new class of data technologies meant to solve the problem of processing large amounts of data quickly.  I first mentioned this in a posting back in March 2009.  Put simply, big data implies datasets so large they can't normally be processed using a standard transactional database.  The term "noSQL" is often used in this context as well. Actually, using parallel processing within the Oracle database combined with Exadata can achieve impressive results.  Look for more from Oracle at OpenWorld as hinted by Jean-Pierre Dijcks. McKinsey recently released a report on big data in which retail was specifically mentioned as an industry that can benefit from the new technologies.  I won't rehash that report because my friend Rama already did such a good job in his posting, Impact of "Big Data" on Retail. The presentation below does a pretty good job of framing the problem, although it doesn't really get into the available technologies (e.g. Exadata, Hadoop, Cassandra, etc.) and isn't retail specific. Determine the Right Analytic Database: A Survey of New Data Technologies So when a retailer asks me about big data, here's what I say:  Big data refers to a set of technologies for processing large volumes of structured and unstructured data.  Imagine collecting everything uttered by your customers on Facebook and Twitter and combining it with all the data you can find about the products you sell (e.g. reviews, images, demonstration videos), including competitive data.  Assuming you could process all that data, you could then personalize offers to specific customers based on their tastes, ensure prices are competitive, and implement better local assortments.  It's really not that far off.

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  • Recurrence relation solution

    - by Travis
    I'm revising past midterms for a final exam this week and am trying to make sense of a solution my professor posted for one of past exams. (You can see the original pdf here, question #6). I'm given the original recurrence relation T(m)=3T(n/2) + n and am told T(1) = 1. I'm pretty sure the solution I've been given is wrong in a few places. The solution is as follows: Let n=2^m T(2^m) = 3T(2^(m-1)) + 2^m 3T(2^(m-1)) = 3^2*T(2^(m-2)) + 2^(m-1)*3 ... 3^(m-1)T(2) = T(1) + 2*3^(m-1) I'm pretty sure this last line is incorrect and they forgot to multiply T(1) by 3^m. He then (tries to) sum the expressions: T(2^m) = 1 + (2^m + 2^(m-1)*3 + ... + 2*3(m-1)) = 1 + 2^m(1 + (3/2)^1 + (3/2)^2 + ... + (3/2)^(m-1)) = 1 + 2^m((3/2)^m-1)*(1/2) = 1 + 3^m - 2^(m-1) = 1 + n^log 3 - n/2 Thus the algorithm is big Theta of (n^log 3). I'm pretty sure that he also got the summation wrong here. By my calculations this should be as follows: T(2^m) = 2^m + 3 * 2^(m-1) + 3^2 * 2^(m-2) + ... + 3^m (3^m because 3^m*T(1) = 3^m should be added, not 1) = 2^m * ((3/2)^1 + (3/2)^2 + ... + (3/2)^m) = 2^m * sum of (3/2)^i from i=0 to m = 2^m * ((3/2)^(m+1) - 1)/(3/2 - 1) = 2^m * ((3/2)^(m+1) - 1)/(1/2) = 2^(m+1) * 3^(m+1)/2^(m+1) - 2^(m+1) = 3^(m+1) - 2 * 2^m Replacing n = 2^m, and from that m = log n T(n) = 3*3^(log n) - 2*n n is O(3^log n), thus the runtime is big Theta of (3^log n) Does this seem right? Thanks for your help!

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  • Fast Data - Big Data's achilles heel

    - by thegreeneman
    At OOW 2013 in Mark Hurd and Thomas Kurian's keynote, they discussed Oracle's Fast Data software solution stack and discussed a number of customers deploying Oracle's Big Data / Fast Data solutions and in particular Oracle's NoSQL Database.  Since that time, there have been a large number of request seeking clarification on how the Fast Data software stack works together to deliver on the promise of real-time Big Data solutions.   Fast Data is a software solution stack that deals with one aspect of Big Data, high velocity.   The software in the Fast Data solution stack involves 3 key pieces and their integration:  Oracle Event Processing, Oracle Coherence, Oracle NoSQL Database.   All three of these technologies address a high throughput, low latency data management requirement.   Oracle Event Processing enables continuous query to filter the Big Data fire hose, enable intelligent chained events to real-time service invocation and augments the data stream to provide Big Data enrichment. Extended SQL syntax allows the definition of sliding windows of time to allow SQL statements to look for triggers on events like breach of weighted moving average on a real-time data stream.    Oracle Coherence is a distributed, grid caching solution which is used to provide very low latency access to cached data when the data is too big to fit into a single process, so it is spread around in a grid architecture to provide memory latency speed access.  It also has some special capabilities to deploy remote behavioral execution for "near data" processing.   The Oracle NoSQL Database is designed to ingest simple key-value data at a controlled throughput rate while providing data redundancy in a cluster to facilitate highly concurrent low latency reads.  For example, when large sensor networks are generating data that need to be captured while analysts are simultaneously extracting the data using range based queries for upstream analytics.  Another example might be storing cookies from user web sessions for ultra low latency user profile management, also leveraging that data using holistic MapReduce operations with your Hadoop cluster to do segmented site analysis.  Understand how NoSQL plays a critical role in Big Data capture and enrichment while simultaneously providing a low latency and scalable data management infrastructure thru clustered, always on, parallel processing in a shared nothing architecture. Learn how easily a NoSQL cluster can be deployed to provide essential services in industry specific Fast Data solutions. See these technologies work together in a demonstration highlighting the salient features of these Fast Data enabling technologies in a location based personalization service. The question then becomes how do these things work together to deliver an end to end Fast Data solution.  The answer is that while different applications will exhibit unique requirements that may drive the need for one or the other of these technologies, often when it comes to Big Data you may need to use them together.   You may have the need for the memory latencies of the Coherence cache, but just have too much data to cache, so you use a combination of Coherence and Oracle NoSQL to handle extreme speed cache overflow and retrieval.   Here is a great reference to how these two technologies are integrated and work together.  Coherence & Oracle NoSQL Database.   On the stream processing side, it is similar as with the Coherence case.  As your sliding windows get larger, holding all the data in the stream can become difficult and out of band data may need to be offloaded into persistent storage.  OEP needs an extreme speed database like Oracle NoSQL Database to help it continue to perform for the real time loop while dealing with persistent spill in the data stream.  Here is a great resource to learn more about how OEP and Oracle NoSQL Database are integrated and work together.  OEP & Oracle NoSQL Database.

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  • Reporting a WCF application's status to F5's Big IP products

    - by ng5000
    In a Windows Server 2003 environment with a self hosted .Net 3.5/WCF application, how can an application report its status to a BigIP Local Traffic Manager? Example: One of my services errors. My custom WCF application hosting software (written because Windows Server 2008 is not yet available and I'm using WCF TCP bindings) detects this and wants to report itself as down until it can recover the errant service. It needs to report itself as down to the BigIP LTM so that it is no longer sent client originated requests.

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  • Bang Notation and Dot Notation in VBA and MS-Access

    - by Nitrodist
    While perusing an application that I'm documenting, I've run across some examples of bang notation in accessing object properties/methods, etc. and in other places they use dot notation for what seems like the same purpose. Is there a difference or preference to using one or the other? Some simple googling only reveals limited information on the subject with some people actually using it in opposite cases. Perhaps there is a coding standards section from MS somewhere that indicates the method of madness?

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  • analysis Big Oh notation psuedocode

    - by tesshu
    I'm having trouble getting my head around algorithm analysis. I seem to be okay identifying linear or squared algorithms but am totally lost with nlogn or logn algorithms, these seem to stem mainly from while loops? Heres an example I was looking at: Algorithm Calculate(A,n) Input: Array A of size n t?0 for i?0 to n-1 do if A[i] is an odd number then Q.enqueue(A[i]) else while Q is not empty do t?t+Q.dequeue() while Q is not empty do t?t+Q.dequeue() return t My best guess is the for loop is executed n times, its nested while loop q times making NQ and the final while loop also Q times resulting in O(NQ +Q) which is linear? I am probably totally wrong. Any help would be much appreciated. thanks

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  • Big-O for calculating all routes from GPS data

    - by HH
    A non-critical GPS module use lists because it needs to be modifiable, new routes added, new distances calculated, continuos comparisons. Well so I thought but my team member wrote something I am very hard to get into. His pseudo code int k =0; a[][] <- create mapModuleNearbyDotList -array //CPU O(n) for(j = 1 to n) // O(nlog(m)) for(i =1 to n) for(k = 1 to n) if(dot is nearby) adj[i][j]=min(adj[i][j], adj[i][k] + adj[k][j]); His ideas transformations of lists to tables His worst case time complexity is O(n^3), where n is number of elements in his so-called table. Exception to the last point with Finite structure: O(mlog(n)) where n is number of vertices and m is the amount of neighbour vertices. Questions about his ideas why to waste resources to transform constantly-modified lists to table? Fast? only point where I to some extent agree but cannot understand the same upper limits n for each for-loops -- perhaps he supposed it circular why does the code take O(mlog(n)) to proceed in time as finite structure? The term finite may be wrong, explicit?

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  • Big-O for GPS data

    - by HH
    A non-critical GPS module use lists because it needs to be modifiable, new routes added, new distances calculated, continuos comparisons. Well so I thought but my team member wrote something I am very hard to get into. His pseudo code int k =0; a[][] <- create mapModuleNearbyDotList -array //CPU O(n) for(j = 1 to n) // O(nlog(m)) for(i =1 to n) for(k = 1 to n) if(dot is nearby) adj[i][j]=min(adj[i][j], adj[i][k] + adj[k][j]); His ideas transformations of lists to tables His worst case time complexity is O(n^3), where n is number of elements in his so-called table. Exception to the last point with Finite structure: O(mlog(n)) where n is number of vertices and m is an arbitrary constants Questions about his ideas why to waste resources to transform constantly-modified lists to table? Fast? only point where I to some extent agree but cannot understand the same upper limits n for each for-loops -- perhaps he supposed it circular why does the code take O(mlog(n)) to proceed in time as finite structure? The term finite may be wrong, explicit?

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  • big background without scrolls

    - by mkoso
    I have layout that has wider background picture than the content area. I have made 970ppx wrapper where the content is. And in body I have backgroud image but I need to have anothen background image above of tht body background image so I have made class bgimg. So basically the markup is like this: But the bgimg is about 1050px wide and thus it gives scrolls when users browser is 1024x768. Is there way of getting rid the scrolls? I mean I want to have have scrollbars if users browrser is narrower thant the 970x wrapper of course. So can I put something like overflow hidden for bgimg class? Hopefully you did understannd what I mean.

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  • Sets, Surrogates, Normalisation, Referential Integrity - the Theory with example Scaling considerati

    - by tonyrogerson
    The Slides and Demo's for the SQLBits session I did today at SQL Bits in London are attached. The Agenda was... Thinking in Sets Surrogate Keys ú What they are ú Comparison NEWID, NEWSEQUENTIALID, IDENTITY ú Fragmenation Normalisation ú An introduction – what is it? Why use it? ú Joins – Pre-filter problems, index intersection ú Fragmentation again Referential Integrity ú Optimiser -> Query rewrite ú Locking considerations around Foreign Keys and Declarative RI (using Triggers)...(read more)

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  • The Grand Unified Framework Theory

    Tom Janssens left a comment: What still bugs me is that we do not have a unified pattern for all .net dev (using modelbinders and icommand for example...) But, Tom we are pretty close. At least as close as we should be, I think. With .NET there are plenty of low level patterns we can reuse regardless of the application platform or architecture. Stuff like: Asynchronous programming with events or the TPL. Object queries with LINQ. Resource management with IDispose. At a higher...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Tackling Big Data Analytics with Oracle Data Integrator

    - by Irem Radzik
    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: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:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman";}  By Mike Eisterer  The term big data draws a lot of attention, but behind the hype there's a simple story. For decades, companies have been making business decisions based on transactional data stored in relational databases. Beyond that critical data, however, is a potential treasure trove of less structured data: weblogs, social media, email, sensors, and documents that can be mined for useful information.  Companies are facing emerging technologies, increasing data volumes, numerous data varieties and the processing power needed to efficiently analyze data which changes with high velocity. Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data sources and analyze them alongside your existing data to find new insights and capitalize on hidden relationships Oracle Data Integrator Enterprise Edition(ODI) is critical to any enterprise big data strategy. ODI and the Oracle Data Connectors provide native access to Hadoop, leveraging such technologies as MapReduce, HDFS and Hive. Alongside with ODI’s metadata driven approach for extracting, loading and transforming data; companies may now integrate their existing data with big data technologies and deliver timely and trusted data to their analytic and decision support platforms. In this session, you’ll learn about ODI and Oracle Big Data Connectors and how, coupled together, they provide the critical integration with multiple big data platforms. Tackling Big Data Analytics with Oracle Data Integrator October 1, 2012 12:15 PM at MOSCONE WEST – 3005 For other data integration sessions at OpenWorld, please check our Focus-On document.  If you are not able to attend OpenWorld, please check out our latest resources for Data Integration.

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  • PageRank is the Best Indicator of Competition Strength For a Keyword in SEO - New Verifiable Theory

    The major argument against PageRank in SEO is that pages with zero PageRank can be in the top positions even for highly competitive keywords. However, we are left with requiring an explanation as to why "PageRank is Google's view of the importance of this page." It becomes apparent that either Google is misleading us or we have all been misinterpreting Google's statement. From extensive evaluation of the top Google search engine results pages for hundreds of keywords, the author observed that those high positioned web pages with PageRanks of zero have a home page with higher PageRanks, usually three or more.

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  • Business Intelligence goes Big Data

    - by Alliances & Channels Redaktion
    Big Data stellt die nächste große Herausforderung für die IT-Branche dar: Massen von Daten aus immer mehr Quellen – aus sozialen Netzwerken, Telekommunikations- und Weblogs, RFID-Lesern etc. – müssen logisch verknüpft, in Echtzeit integriert und verarbeitet werden. Doch wie sieht es mit der praktischen Umsetzung aus? Eine europaweite Studie von Steria Mummert Consulting zeigt: Lediglich 28 % der Unternehmen haben bereits heute eine übergreifende, abgestimmte Business-Intelligence-Strategie implementiert. Vorherrschend sind BI-Insellösungen, die schon jetzt an den Grenzen ihrer Kapazität arbeiten. Daten werden also bisher nur eingeschränkt als wertschöpfende Ressource genutzt! Das Ergebnis der Studie klingt erschreckend, doch Unternehmen können es zu Ihrem Vorteil nutzen: Wer jetzt das Thema Big Data anpackt, kann sich einen gewinnbringenden Vorsprung vor dem Wettbewerb sichern. Wie sieht die Analyse-Umgebung der Zukunft aus? Wie und wo kann Big Data für den Geschäftserfolg genutzt werden? Antworten darauf liefert die Kunden-Event Reihe von Oracle und dem Oracle Platinum Partner Steria Mummert Consulting: Hier werden Strategien entwickelt, wie Unternehmen mit Information Discovery ihr BI-Potenzial auf dem Weg zur Big Data Schritt für Schritt ausbauen können. Highlights aus München Durchweg positives Feedback haben wir aus München, der ersten Station der Eventreihe am 23.7., erhalten: Nicht nur die tolle Location, das "La Villa" im Bamberger Haus, überzeugte. Die 31 Teilnehmerinnen und Teilnehmer konnten auch inhaltlich eine Menge mitnehmen – unter anderem einen konkreten Vorschlag für ihre eigene Roadmap in Richtung Big Data. Die Ausgangsfrage des Tages lautete – einfach und umfassend zugleich: Wie können wir den Überblick in einer komplexen Welt behalten? Den Status quo in Europa für Business Intelligence präsentierte Steria Mummert Consulting entlang der Europäischen biMA®-Studie 2012/13. Anhand von Anwendungsbeispielen aus ihrer Praxis präsentierten die geladenen Experten von Oracle und Steria Mummert Consulting verschiedene Lösungsansätze. Eine sehr anschauliche Demo zu Endeca zeigte beispielsweise, wie einfach und flexibel ein Dashboard sein kann: Hier gibt es keine vordefinierten Reports, stattdessen können Entscheider die Filter einfach per Drag & Drop verändern und bekommen so einen individuell sturkturierten Überblick über ihre Daten. Einen Ausblick bot die Session zu Oracle Business Analytics für mobile Anwendungen und Real-Time Decisions. Fazit: eine gelungene Mischung aus Überblicks-Informationen und ganz konkreten Ideen für die spezifischen Anwendungsbereiche der Kunden. Die Eventreihe „BI goes Big Data“ macht im August in Hamburg und Frankfurt Station. Die kostenfreie Veranstaltung findet zusammen mit Steria Mummert Consulting statt und richtet sich an Endkunden. In Hamburg am 14.8.2013 – zur AnmeldungIn Frankfurt a.M. am 20.8.2013 – zur Anmeldung

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