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• How to get the data for intra-day candlestick charts for stocks on eg Nasdaq

  - by Chris

  Hi, For a learning exercise, i'm wanting to create candlestick (stock) graphs for stocks using zedgraph. Now on google finance, i can get daily open-high-low-close data which is perfect for making these graphs, but i'm wanting to create intra-day graphs, eg open-high-low-close data for an hour (or 5 mins, or 1 min even). Is there any way to get that kind of data without having to subscribe to any expensive service? I've heard opentick mentioned in an old SO question, but their site is defunct now. I was thinking of polling google finance once a minute to get the latest stock price, then with an hour's worth of 60 prices, i could then roughly calculate the open-high-low-close, but this is a bit of an estimation and i'm open to other suggestions. Cheers all.

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• Latent Dirichlet Allocation, pitfalls, tips and programs

  - by Gregg Lind

  I'm experimenting with Latent Dirichlet Allocation for topic disambiguation and assignment, and I'm looking for advice. Which program is the "best", where best is some combination of easiest to use, best prior estimation, fast How do I incorporate my intuitions about topicality. Let's say I think I know that some items in the corpus are really in the same category, like all articles by the same author. Can I add that into the analysis? Any unexpected pitfalls or tips I should know before embarking? I'd prefer is there are R or Python front ends for whatever program, but I expect (and accept) that I'll be dealing with C.

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• Onshore work methods V Offshore Supplier work methods - how to strike a balance?

  - by LadyCoconut

  Any advice on the best way to strike a balance between the work methods of an offshore supplier and the work methods of a new onshore team? We have an offshore supplier with about 2 years who have their own working practices and methods. I was bought in as the first onshore developer for my company with the view to vetting the code that comes in and putting together some best practices. Now from what I've seen there are lots of holes in their process (e.g. estimation, planning, code reviews, coding standards from about 10 years ago, no concept of mocking, refactoring etc). I need to be seen as a problem solver and not a problem creator but also I need to try and be somewhat forceful of what they are doing needs improving and at the end of the day they are a supplier. I would appreciate any advice. Thanks.

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• Blog Engine .NET with XML Data Storage How is it so fast ?

  - by Kubi

  I am looking for a way to embed a blog engine into my own application and I am too curious about Blog Engine algorithm. This may not be the correct place to ask but, How is that possible to store blog entry data in an XML file like BlogEngine.Net with Default Configuration. It must be getting slower everyday while the file is getting larger and larger. I am wondering the algorithm behind that. Is it loading with a different way ? Or Am I wrong with the time estimation ? I know it is open source but I thought it would be better to see a discussion here for some others might be thinking the same and this thread can be a reference.

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• Using AsyncTask to display data in ListView, but onPostExecute not being called

  - by sumisu

  I made a simple AsyncTask class to display data in ListView with the help of this stackoverflow question. But the AsyncTask onPostExecute is not being called. This is my code: public class Start extends SherlockActivity { // JSON Node names private static final String TAG_ID = "id"; private static final String TAG_NAME = "name"; // category JSONArray JSONArray category = null; private ListView lv; @Override public void onCreate(Bundle savedInstanceState) { setTheme(SampleList.THEME); //Used for theme switching in samples super.onCreate(savedInstanceState); setContentView(R.layout.test); new MyAsyncTask().execute("http://...."); // Launching new screen on Selecting Single ListItem lv.setOnItemClickListener(new OnItemClickListener() { @Override public void onItemClick(AdapterView<?> parent, View view, int position, long id) { // getting values from selected ListItem String name = ((TextView) view.findViewById(R.id.name)).getText().toString(); String cost = ((TextView) view.findViewById(R.id.mail)).getText().toString(); // Starting new intent Intent in = new Intent(getApplicationContext(), SingleMenuItemActivity.class); in.putExtra("categoryname", name); System.out.println(cost); in.putExtra("categoryid", cost); startActivity(in); } }); } public class MyAsyncTask extends AsyncTask<String, Void, ArrayList<HashMap<String, String>> > { // Hashmap for ListView ArrayList<HashMap<String, String>> contactList = new ArrayList<HashMap<String, String>>(); @Override protected ArrayList<HashMap<String, String>> doInBackground(String... params) { // Creating JSON Parser instance JSONParser jParser = new JSONParser(); // getting JSON string from URL category = jParser.getJSONArrayFromUrl(params[0]); try { // looping through All Contacts for(int i = 0; i < category.length(); i++){ JSONObject c = category.getJSONObject(i); // Storing each json item in variable String id = c.getString(TAG_ID); String name = c.getString(TAG_NAME); // creating new HashMap HashMap<String, String> map = new HashMap<String, String>(); // adding each child node to HashMap key => value map.put(TAG_ID, id); map.put(TAG_NAME, name); // adding HashList to ArrayList contactList.add(map); } } catch (JSONException e) { Log.e("log_tag", "Error parsing data "+e.toString()); } return contactList; } @Override protected void onPostExecute(ArrayList<HashMap<String, String>> result) { ListAdapter adapter = new SimpleAdapter(Start.this, result , R.layout.list_item, new String[] { TAG_NAME, TAG_ID }, new int[] { R.id.name, R.id.mail }); // selecting single ListView item lv = (ListView) findViewById(R.id.ListView); lv.setAdapter(adapter); } } } Eclipse: 11-25 11:40:31.896: E/AndroidRuntime(917): java.lang.RuntimeException: Unable to start activity ComponentInfo{de.essentials/de.main.Start}: java.lang.NullPointerException

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• Is it possible to implement bitwise operators using integer arithmetic?

  - by Statement

  Hello World! I am facing a rather peculiar problem. I am working on a compiler for an architecture that doesn't support bitwise operations. However, it handles signed 16 bit integer arithmetics and I was wondering if it would be possible to implement bitwise operations using only: Addition (c = a + b) Subtraction (c = a - b) Division (c = a / b) Multiplication (c = a * b) Modulus (c = a % b) Minimum (c = min(a, b)) Maximum (c = max(a, b)) Comparisons (c = (a < b), c = (a == b), c = (a <= b), et.c.) Jumps (goto, for, et.c.) The bitwise operations I want to be able to support are: Or (c = a | b) And (c = a & b) Xor (c = a ^ b) Left Shift (c = a << b) Right Shift (c = a b) (All integers are signed so this is a problem) Signed Shift (c = a b) One's Complement (a = ~b) (Already found a solution, see below) Normally the problem is the other way around; how to achieve arithmetic optimizations using bitwise hacks. However not in this case. Writable memory is very scarce on this architecture, hence the need for bitwise operations. The bitwise functions themselves should not use a lot of temporary variables. However, constant read-only data & instruction memory is abundant. A side note here also is that jumps and branches are not expensive and all data is readily cached. Jumps cost half the cycles as arithmetic (including load/store) instructions do. On other words, all of the above supported functions cost twice the cycles of a single jump. Some thoughts that might help: I figured out that you can do one's complement (negate bits) with the following code: // Bitwise one's complement b = ~a; // Arithmetic one's complement b = -1 - a; I also remember the old shift hack when dividing with a power of two so the bitwise shift can be expressed as: // Bitwise left shift b = a << 4; // Arithmetic left shift b = a * 16; // 2^4 = 16 // Signed right shift b = a >>> 4; // Arithmetic right shift b = a / 16; For the rest of the bitwise operations I am slightly clueless. I wish the architects of this architecture would have supplied bit-operations. I would also like to know if there is a fast/easy way of computing the power of two (for shift operations) without using a memory data table. A naive solution would be to jump into a field of multiplications: b = 1; switch (a) { case 15: b = b * 2; case 14: b = b * 2; // ... exploting fallthrough (instruction memory is magnitudes larger) case 2: b = b * 2; case 1: b = b * 2; } Or a Set & Jump approach: switch (a) { case 15: b = 32768; break; case 14: b = 16384; break; // ... exploiting the fact that a jump is faster than one additional mul // at the cost of doubling the instruction memory footprint. case 2: b = 4; break; case 1: b = 2; break; }

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• Python: Memory usage and optimization when modifying lists

  - by xApple

  The problem My concern is the following: I am storing a relativity large dataset in a classical python list and in order to process the data I must iterate over the list several times, perform some operations on the elements, and often pop an item out of the list. It seems that deleting one item out of a Python list costs O(N) since Python has to copy all the items above the element at hand down one place. Furthermore, since the number of items to delete is approximately proportional to the number of elements in the list this results in an O(N^2) algorithm. I am hoping to find a solution that is cost effective (time and memory-wise). I have studied what I could find on the internet and have summarized my different options below. Which one is the best candidate ? Keeping a local index: while processingdata: index = 0 while index < len(somelist): item = somelist[index] dosomestuff(item) if somecondition(item): del somelist[index] else: index += 1 This is the original solution I came up with. Not only is this not very elegant, but I am hoping there is better way to do it that remains time and memory efficient. Walking the list backwards: while processingdata: for i in xrange(len(somelist) - 1, -1, -1): dosomestuff(item) if somecondition(somelist, i): somelist.pop(i) This avoids incrementing an index variable but ultimately has the same cost as the original version. It also breaks the logic of dosomestuff(item) that wishes to process them in the same order as they appear in the original list. Making a new list: while processingdata: for i, item in enumerate(somelist): dosomestuff(item) newlist = [] for item in somelist: if somecondition(item): newlist.append(item) somelist = newlist gc.collect() This is a very naive strategy for eliminating elements from a list and requires lots of memory since an almost full copy of the list must be made. Using list comprehensions: while processingdata: for i, item in enumerate(somelist): dosomestuff(item) somelist[:] = [x for x in somelist if somecondition(x)] This is very elegant but under-the-cover it walks the whole list one more time and must copy most of the elements in it. My intuition is that this operation probably costs more than the original del statement at least memory wise. Keep in mind that somelist can be huge and that any solution that will iterate through it only once per run will probably always win. Using the filter function: while processingdata: for i, item in enumerate(somelist): dosomestuff(item) somelist = filter(lambda x: not subtle_condition(x), somelist) This also creates a new list occupying lots of RAM. Using the itertools' filter function: from itertools import ifilterfalse while processingdata: for item in itertools.ifilterfalse(somecondtion, somelist): dosomestuff(item) This version of the filter call does not create a new list but will not call dosomestuff on every item breaking the logic of the algorithm. I am including this example only for the purpose of creating an exhaustive list. Moving items up the list while walking while processingdata: index = 0 for item in somelist: dosomestuff(item) if not somecondition(item): somelist[index] = item index += 1 del somelist[index:] This is a subtle method that seems cost effective. I think it will move each item (or the pointer to each item ?) exactly once resulting in an O(N) algorithm. Finally, I hope Python will be intelligent enough to resize the list at the end without allocating memory for a new copy of the list. Not sure though. Abandoning Python lists: class Doubly_Linked_List: def __init__(self): self.first = None self.last = None self.n = 0 def __len__(self): return self.n def __iter__(self): return DLLIter(self) def iterator(self): return self.__iter__() def append(self, x): x = DLLElement(x) x.next = None if self.last is None: x.prev = None self.last = x self.first = x self.n = 1 else: x.prev = self.last x.prev.next = x self.last = x self.n += 1 class DLLElement: def __init__(self, x): self.next = None self.data = x self.prev = None class DLLIter: etc... This type of object resembles a python list in a limited way. However, deletion of an element is guaranteed O(1). I would not like to go here since this would require massive amounts of code refactoring almost everywhere.

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• Estimate serialization size of objects?

  - by Stefan K.

  In my thesis, I woud like to enhance messaging in a cluster. It's important to log runtime information about how big a message is (should I prefer processing local or remote). I could just find frameoworks about estimating the object memory size based on java instrumentation. I've tested classmexer, which didn't come close to the serialization size and sourceforge SizeOf. In a small testcase, SizeOf was around 10% wrong and 10x faster than serialization. (Still transient breaks the estimation completely and since e.g. ArrayList is transient but is serialized as an Array, it's not easy to patch SizeOf. But I could live with that) On the other hand, 10x faster with 10% error doesn't seem very good. Any ideas how I could do better?

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• Better algorithm for estimating download time

  - by Scott Smith

  We've all seen the download time running estimate that initially says something like "7 days", but keeps dropping wildly (e.g. "23 hours", "45 minutes", "1 min. 50 sec", etc) with each successive estimation as the chunks are downloaded. To avoid these initial (alarming) estimates, there are techniques one could try like suppressing display of the first n estimates, or waiting for the delta between estimates to drop below some threshold before you start displaying them, but these don't seem like a general, robust solution. There are corner cases involving too few samples, or samples that actually are wildly varying... I think I recall a general solution for this kind of thing in mathematics (statistics?) that reduced or eliminated these wild values. Does anyone know?

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• <function> referenced from; symbol(s) not found.

  - by jfm429

  I have a piece of C code that is used from a C++ function. At the top of my C++ file I have the line: #include "prediction.h" In prediction.h I have this: #ifndef prediction #define prediction #include "structs.h" typedef struct { double estimation; double variance; } response; response runPrediction(int obs, location* positions, double* observations, int targets, location* targetPositions); #endif I also have prediction.c, which has: #include "prediction.h" response runPrediction(int obs, location* positions, double* observations, int targets, location* targetPositions) { // code here } Now, in my C++ file (which as I said includes prediction.h) I call that function, then compile (through Xcode) I get this error: "runPrediction(int, location*, double*, int, location*)", referenced from: mainFrame::respondTo(char*, int)in mainFrame.o ld: symbol(s) not found collect2: ld returned 1 exit status prediction.c is marked for compilation for the current target. I don't have any problems with other .cpp files not being compiled. Any thoughts here?

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• How to resolve strange conflict between form post and ajax post?

  - by Oliver Hyde

  On the one page, I am trying to use ajax to edit existing values. I am doing this by using jQuery Inline Edit and posting away the new data, updating the record and returning with success. This is working fine. Next I have implemented the ability to add new records, to do this I have a form at the end of the table, which submits post data then redirects back to the original page. Each of them work individually, but after I have used the form to add a new record, the inline editing stops to work. If I close the webpage and reopen it, it works fine again until I have used the form and it goes of the rails again. I have tried a number of solutions, clearing session data, giving the form a separate name, redirecting to an alternative page (which does work, but is not ideal as I want the form to redirect back to the original location ). Here is a sample of the view form data: <?php foreach($week->incomes as $income):?> <tr> <td><?php echo $income->name;?></td> <td width="70" style="text-align:right;" class="editableSingle income id<?php echo $income->id;?>">$<?php echo $income->cost;?></td> </tr> <?php endforeach;?> <?php echo form_open('budget/add/'.$week->id.'/income/index', 'class="form-vertical" id="add_income"'); ?> <tr> <td> <input type="text" name="name" class="input-small" placeholder="Name"> <input type="text" name="cost" class="input-small" placeholder="Cost"> </td> <td> <button type="submit" class="btn btn-small pull-right"><i class="icon-plus "></i></button> </td> </tr> <?php echo form_close(); ?> This is the javascript initialisation code: $(function(){ $.inlineEdit({ income: 'budget/update_income/', expense: 'budget/update_expense/' }, { animate: false, filterElementValue: function($o){ if ($o.hasClass('income')) { return $o.html().match(/\$(.+)/)[1]; } else if ($o.hasClass('expense')) { return $o.html().match(/\$(.+)/)[1]; } else { return $o.html(); } }, afterSave: function(o){ if (o.type == 'income') { $('.income.id' + o.id).prepend('$'); } if (o.type == 'expense') { $('.expense.id' + o.id).prepend('$'); } }, colors: { error:'green' } }); }); If I can provide any more information to clarify what I have attempted etc, let me know. Temporary Fix It seems I have come up with a work around, not ideal as I still am not sure what is causing the issue. I have created a method called redirect. public function redirect(){ redirect(''); } am now calling that after the form submit which has temporarily allows my multiple post submits to work.

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• OpenGL Vertex Array/Buffer Objects

  - by sadanjon

  Question 1 Do vertex buffer objects created under a certain VAO deleted once that VAO is deleted? An example: glGenBuffers(1, &bufferObject); glGenVertexArrays(1, &VAO); glBindVertexArray(VAO); glBindBuffer(GL_ARRAY_BUFFER, bufferObject); glBufferData(GL_ARRAY_BUFFER, sizeof(someVertices), someVertices, GL_STATIC_DRAW); glEnableVertexAttribArray(positionAttrib); glVertexAttribPointer(positionAttrib, 3, GL_FLOAT, GL_FALSE, 0, NULL); When later calling glDeleteVertexArrays(1, &VAO);, will bufferObject be deleted as well? The reason I'm asking is that I saw a few examples over the web that didn't delete those buffer objects. Question 2 What is the maximum amount of memory that I can allocate for buffer objects? It must be system dependent of course, but I can't seem find an estimation for it. What happens when video RAM isn't big enough? How would I know?

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• How to decrease front end development time in a company/team environment?

  - by metal-gear-solid

  How to decrease front end development time in a company/team environment? My company is asking to suggest idea to make front end development process faster? Some points I realized main problem is client never provide right information at first time and many front end developer works on same project on same CSS so everyone makes his own method sometimes. It increase time of process. Graceful degradation and progressive enhancement both takes time to think and development. should we think about it? it increase the project cost. How to judge time estimation by just seeing a PSD for to make PSD in Cross browser Compatible XHTML CSS. Most of the time I always give less time then then takes more time. Any other suggestions to improve work efficiency in a team (50 people) environment?

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• OWB 11gR2 - Early Arriving Facts

  - by Dawei Sun

  A common challenge when building ETL components for a data warehouse is how to handle early arriving facts. OWB 11gR2 introduced a new feature to address this for dimensional objects entitled Orphan Management. An orphan record is one that does not have a corresponding existing parent record. Orphan management automates the process of handling source rows that do not meet the requirements necessary to form a valid dimension or cube record. In this article, a simple example will be provided to show you how to use Orphan Management in OWB. We first import a sample MDL file that contains all the objects we need. Then we take some time to examine all the objects. After that, we prepare the source data, deploy the target table and dimension/cube loading map. Finally, we run the loading maps, and check the data in target dimension/cube tables. OK, let’s start… 1. Import MDL file and examine sample project First, download zip file from here, which includes a MDL file and three source data files. Then we open OWB design center, import orphan_management.mdl by using the menu File->Import->Warehouse Builder Metadata. Now we have several objects in BI_DEMO project as below: Mapping LOAD_CHANNELS_OM: The mapping for dimension loading. Mapping LOAD_SALES_OM: The mapping for cube loading. Dimension CHANNELS_OM: The dimension that contains channels data. Cube SALES_OM: The cube that contains sales data. Table CHANNELS_OM: The star implementation table of dimension CHANNELS_OM. Table SALES_OM: The star implementation table of cube SALES_OM. Table SRC_CHANNELS: The source table of channels data, that will be loaded into dimension CHANNELS_OM. Table SRC_ORDERS and SRC_ORDER_ITEMS: The source tables of sales data that will be loaded into cube SALES_OM. Sequence CLASS_OM_DIM_SEQ: The sequence used for loading dimension CHANNELS_OM. Dimension CHANNELS_OM This dimension has a hierarchy with three levels: TOTAL, CLASS and CHANNEL. Each level has three attributes: ID (surrogate key), NAME and SOURCE_ID (business key). It has a standard star implementation. The orphan management policy and the default parent setting are shown in the following screenshots: The orphan management policy options that you can set for loading are: Reject Orphan: The record is not inserted. Default Parent: You can specify a default parent record. This default record is used as the parent record for any record that does not have an existing parent record. If the default parent record does not exist, Warehouse Builder creates the default parent record. You specify the attribute values of the default parent record at the time of defining the dimensional object. If any ancestor of the default parent does not exist, Warehouse Builder also creates this record. No Maintenance: This is the default behavior. Warehouse Builder does not actively detect, reject, or fix orphan records. While removing data from a dimension, you can select one of the following orphan management policies: Reject Removal: Warehouse Builder does not allow you to delete the record if it has existing child records. No Maintenance: This is the default behavior. Warehouse Builder does not actively detect, reject, or fix orphan records. (More details are at http://download.oracle.com/docs/cd/E11882_01/owb.112/e10935/dim_objects.htm#insertedID1) Cube SALES_OM This cube is references to dimension CHANNELS_OM. It has three measures: AMOUNT, QUANTITY and COST. The orphan management policy setting are shown as following screenshot: The orphan management policy options that you can set for loading are: No Maintenance: Warehouse Builder does not actively detect, reject, or fix orphan rows. Default Dimension Record: Warehouse Builder assigns a default dimension record for any row that has an invalid or null dimension key value. Use the Settings button to define the default parent row. Reject Orphan: Warehouse Builder does not insert the row if it does not have an existing dimension record. (More details are at http://download.oracle.com/docs/cd/E11882_01/owb.112/e10935/dim_objects.htm#BABEACDG) Mapping LOAD_CHANNELS_OM This mapping loads source data from table SRC_CHANNELS to dimension CHANNELS_OM. The operator CHANNELS_IN is bound to table SRC_CHANNELS; CHANNELS_OUT is bound to dimension CHANNELS_OM. The TOTALS operator is used for generating a constant value for the top level in the dimension. The CLASS_FILTER operator is used to filter out the “invalid” class name, so then we can see what will happen when those channel records with an “invalid” parent are loading into dimension. Some properties of the dimension operator in this mapping are important to orphan management. See the screenshot below: Create Default Level Records: If YES, then default level records will be created. This property must be set to YES for dimensions and cubes if one of their orphan management policies is “Default Parent” or “Default Dimension Record”. This property is set to NO by default, so the user may need to set this to YES manually. LOAD policy for INVALID keys/ LOAD policy for NULL keys: These two properties have the same meaning as in the dimension editor. The values are set to the same as the dimension value when user drops the dimension into the mapping. The user does not need to modify these properties. Record Error Rows: If YES, error rows will be inserted into error table when loading the dimension. REMOVE Orphan Policy: This property is used when removing data from a dimension. Since the dimension loading type is set to LOAD in this example, this property is disabled. Mapping LOAD_SALES_OM This mapping loads source data from table SRC_ORDERS and SRC_ORDER_ITEMS to cube SALES_OM. This mapping seems a little bit complicated, but operators in the red rectangle are used to filter out and generate the records with “invalid” or “null” dimension keys. Some properties of the cube operator in a mapping are important to orphan management. See the screenshot below: Enable Source Aggregation: Should be checked in this example. If the default dimension record orphan policy is set for the cube operator, then it is recommended that source aggregation also be enabled. Otherwise, the orphan management processing may produce multiple fact rows with the same default dimension references, which will cause an “unstable rowset” execution error in the database, since the dimension refs are used as update match attributes for updating the fact table. LOAD policy for INVALID keys/ LOAD policy for NULL keys: These two properties have the same meaning as in the cube editor. The values are set to the same as in the cube editor when the user drops the cube into the mapping. The user does not need to modify these properties. Record Error Rows: If YES, error rows will be inserted into error table when loading the cube. 2. Deploy objects and mappings We now can deploy the objects. First, make sure location SALES_WH_LOCAL has been correctly configured. Then open Control Center Manager by using the menu Tools->Control Center Manager. Expand BI_DEMO->SALES_WH_LOCAL, click SALES_WH node on the project tree. We can see the following objects: Deploy all the objects in the following order: Sequence CLASS_OM_DIM_SEQ Table CHANNELS_OM, SALES_OM, SRC_CHANNELS, SRC_ORDERS, SRC_ORDER_ITEMS Dimension CHANNELS_OM Cube SALES_OM Mapping LOAD_CHANNELS_OM, LOAD_SALES_OM Note that we deployed source tables as well. Normally, we import source table from database instead of deploying them to target schema. However, in this example, we designed the source tables in OWB and deployed them to database for the purpose of this demonstration. 3. Prepare and examine source data Before running the mappings, we need to populate and examine the source data first. Run SRC_CHANNELS.sql, SRC_ORDERS.sql and SRC_ORDER_ITEMS.sql as target user. Then we check the data in these three tables. Table SRC_CHANNELS SQL> select rownum, id, class, name from src_channels; Records 1~5 are correct; they should be loaded into dimension without error. Records 6,7 and 8 have null parents; they should be loaded into dimension with a default parent value, and should be inserted into error table at the same time. Records 9, 10 and 11 have “invalid” parents; they should be rejected by dimension, and inserted into error table. Table SRC_ORDERS and SRC_ORDER_ITEMS SQL> select rownum, a.id, a.channel, b.amount, b.quantity, b.cost from src_orders a, src_order_items b where a.id = b.order_id; Record 178 has null dimension reference; it should be loaded into cube with a default dimension reference, and should be inserted into error table at the same time. Record 179 has “invalid” dimension reference; it should be rejected by cube, and inserted into error table. Other records should be aggregated and loaded into cube correctly. 4. Run the mappings and examine the target data In the Control Center Manager, expand BI_DEMO-> SALES_WH_LOCAL-> SALES_WH-> Mappings, right click on LOAD_CHANNELS_OM node, click Start. Use the same way to run mapping LOAD_SALES_OM. When they successfully finished, we can check the data in target tables. Table CHANNELS_OM SQL> select rownum, total_id, total_name, total_source_id, class_id,class_name, class_source_id, channel_id, channel_name,channel_source_id from channels_om order by abs(dimension_key); Records 1,2 and 3 are the default dimension records for the three levels. Records 8, 10 and 15 are the loaded records that originally have null parents. We see their parents name (class_name) is set to DEF_CLASS_NAME. Those records whose CHANNEL_NAME are Special_4, Special_5 and Special_6 are not loaded to this table because of the invalid parent. Error Table CHANNELS_OM_ERR SQL> select rownum, class_source_id, channel_id, channel_name,channel_source_id, err$$$_error_reason from channels_om_err order by channel_name; We can see all the record with null parent or invalid parent are inserted into this error table. Error reason is “Default parent used for record” for the first three records, and “No parent found for record” for the last three. Table SALES_OM SQL> select a.*, b.channel_name from sales_om a, channels_om b where a.channels=b.channel_id; We can see the order record with null channel_name has been loaded into target table with a default channel_name. The one with “invalid” channel_name are not loaded. Error Table SALES_OM_ERR SQL> select a.amount, a.cost, a.quantity, a.channels, b.channel_name, a.err$$$_error_reason from sales_om_err a, channels_om b where a.channels=b.channel_id(+); We can see the order records with null or invalid channel_name are inserted into error table. If the dimension reference column is null, the error reason is “Default dimension record used for fact”. If it is invalid, the error reason is “Dimension record not found for fact”. Summary In summary, this article illustrated the Orphan Management feature in OWB 11gR2. Automated orphan management policies improve ETL developer and administrator productivity by addressing an important cause of cube and dimension load failures, without requiring developers to explicitly build logic to handle these orphan rows.

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• Performance Optimization &ndash; It Is Faster When You Can Measure It

  - by Alois Kraus

  Performance optimization in bigger systems is hard because the measured numbers can vary greatly depending on the measurement method of your choice. To measure execution timing of specific methods in your application you usually use Time Measurement Method Potential Pitfalls Stopwatch Most accurate method on recent processors. Internally it uses the RDTSC instruction. Since the counter is processor specific you can get greatly different values when your thread is scheduled to another core or the core goes into a power saving mode. But things do change luckily: Intel's Designer's vol3b, section 16.11.1 "16.11.1 Invariant TSC The time stamp counter in newer processors may support an enhancement, referred to as invariant TSC. Processor's support for invariant TSC is indicated by CPUID.80000007H:EDX[8]. The invariant TSC will run at a constant rate in all ACPI P-, C-. and T-states. This is the architectural behavior moving forward. On processors with invariant TSC support, the OS may use the TSC for wall clock timer services (instead of ACPI or HPET timers). TSC reads are much more efficient and do not incur the overhead associated with a ring transition or access to a platform resource." DateTime.Now Good but it has only a resolution of 16ms which can be not enough if you want more accuracy.   Reporting Method Potential Pitfalls Console.WriteLine Ok if not called too often. Debug.Print Are you really measuring performance with Debug Builds? Shame on you. Trace.WriteLine Better but you need to plug in some good output listener like a trace file. But be aware that the first time you call this method it will read your app.config and deserialize your system.diagnostics section which does also take time.   In general it is a good idea to use some tracing library which does measure the timing for you and you only need to decorate some methods with tracing so you can later verify if something has changed for the better or worse. In my previous article I did compare measuring performance with quantum mechanics. This analogy does work surprising well. When you measure a quantum system there is a lower limit how accurately you can measure something. The Heisenberg uncertainty relation does tell us that you cannot measure of a quantum system the impulse and location of a particle at the same time with infinite accuracy. For programmers the two variables are execution time and memory allocations. If you try to measure the timings of all methods in your application you will need to store them somewhere. The fastest storage space besides the CPU cache is the memory. But if your timing values do consume all available memory there is no memory left for the actual application to run. On the other hand if you try to record all memory allocations of your application you will also need to store the data somewhere. This will cost you memory and execution time. These constraints are always there and regardless how good the marketing of tool vendors for performance and memory profilers are: Any measurement will disturb the system in a non predictable way. Commercial tool vendors will tell you they do calculate this overhead and subtract it from the measured values to give you the most accurate values but in reality it is not entirely true. After falling into the trap to trust the profiler timings several times I have got into the habit to Measure with a profiler to get an idea where potential bottlenecks are. Measure again with tracing only the specific methods to check if this method is really worth optimizing. Optimize it Measure again. Be surprised that your optimization has made things worse. Think harder Implement something that really works. Measure again Finished! - Or look for the next bottleneck. Recently I have looked into issues with serialization performance. For serialization DataContractSerializer was used and I was not sure if XML is really the most optimal wire format. After looking around I have found protobuf-net which uses Googles Protocol Buffer format which is a compact binary serialization format. What is good for Google should be good for us. A small sample app to check out performance was a matter of minutes: using ProtoBuf; using System; using System.Diagnostics; using System.IO; using System.Reflection; using System.Runtime.Serialization; [DataContract, Serializable] class Data { [DataMember(Order=1)] public int IntValue { get; set; } [DataMember(Order = 2)] public string StringValue { get; set; } [DataMember(Order = 3)] public bool IsActivated { get; set; } [DataMember(Order = 4)] public BindingFlags Flags { get; set; } } class Program { static MemoryStream _Stream = new MemoryStream(); static MemoryStream Stream { get { _Stream.Position = 0; _Stream.SetLength(0); return _Stream; } } static void Main(string[] args) { DataContractSerializer ser = new DataContractSerializer(typeof(Data)); Data data = new Data { IntValue = 100, IsActivated = true, StringValue = "Hi this is a small string value to check if serialization does work as expected" }; var sw = Stopwatch.StartNew(); int Runs = 1000 * 1000; for (int i = 0; i < Runs; i++) { //ser.WriteObject(Stream, data); Serializer.Serialize<Data>(Stream, data); } sw.Stop(); Console.WriteLine("Did take {0:N0}ms for {1:N0} objects", sw.Elapsed.TotalMilliseconds, Runs); Console.ReadLine(); } } The results are indeed promising: Serializer Time in ms N objects protobuf-net   807 1000000 DataContract 4402 1000000 Nearly a factor 5 faster and a much more compact wire format. Lets use it! After switching over to protbuf-net the transfered wire data has dropped by a factor two (good) and the performance has worsened by nearly a factor two. How is that possible? We have measured it? Protobuf-net is much faster! As it turns out protobuf-net is faster but it has a cost: For the first time a type is de/serialized it does use some very smart code-gen which does not come for free. Lets try to measure this one by setting of our performance test app the Runs value not to one million but to 1. Serializer Time in ms N objects protobuf-net 85 1 DataContract 24 1 The code-gen overhead is significant and can take up to 200ms for more complex types. The break even point where the code-gen cost is amortized by its faster serialization performance is (assuming small objects) somewhere between 20.000-40.000 serialized objects. As it turned out my specific scenario involved about 100 types and 1000 serializations in total. That explains why the good old DataContractSerializer is not so easy to take out of business. The final approach I ended up was to reduce the number of types and to serialize primitive types via BinaryWriter directly which turned out to be a pretty good alternative. It sounded good until I measured again and found that my optimizations so far do not help much. After looking more deeper at the profiling data I did found that one of the 1000 calls did take 50% of the time. So how do I find out which call it was? Normal profilers do fail short at this discipline. A (totally undeserved) relatively unknown profiler is SpeedTrace which does unlike normal profilers create traces of your applications by instrumenting your IL code at runtime. This way you can look at the full call stack of the one slow serializer call to find out if this stack was something special. Unfortunately the call stack showed nothing special. But luckily I have my own tracing as well and I could see that the slow serializer call did happen during the serialization of a bool value. When you encounter after much analysis something unreasonable you cannot explain it then the chances are good that your thread was suspended by the garbage collector. If there is a problem with excessive GCs remains to be investigated but so far the serialization performance seems to be mostly ok.  When you do profile a complex system with many interconnected processes you can never be sure that the timings you just did measure are accurate at all. Some process might be hitting the disc slowing things down for all other processes for some seconds as well. There is a big difference between warm and cold startup. If you restart all processes you can basically forget the first run because of the OS disc cache, JIT and GCs make the measured timings very flexible. When you are in need of a random number generator you should measure cold startup times of a sufficiently complex system. After the first run you can try again getting different and much lower numbers. Now try again at least two times to get some feeling how stable the numbers are. Oh and try to do the same thing the next day. It might be that the bottleneck you found yesterday is gone today. Thanks to GC and other random stuff it can become pretty hard to find stuff worth optimizing if no big bottlenecks except bloatloads of code are left anymore. When I have found a spot worth optimizing I do make the code changes and do measure again to check if something has changed. If it has got slower and I am certain that my change should have made it faster I can blame the GC again. The thing is that if you optimize stuff and you allocate less objects the GC times will shift to some other location. If you are unlucky it will make your faster working code slower because you see now GCs at times where none were before. This is where the stuff does get really tricky. A safe escape hatch is to create a repro of the slow code in an isolated application so you can change things fast in a reliable manner. Then the normal profilers do also start working again. As Vance Morrison does point out it is much more complex to profile a system against the wall clock compared to optimize for CPU time. The reason is that for wall clock time analysis you need to understand how your system does work and which threads (if you have not one but perhaps 20) are causing a visible delay to the end user and which threads can wait a long time without affecting the user experience at all. Next time: Commercial profiler shootout.

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• New Communications Industry Data Model with "Factory Installed" Predictive Analytics using Oracle Da

  - by charlie.berger

  Oracle Introduces Oracle Communications Data Model to Provide Actionable Insight for Communications Service Providers   We've integrated pre-installed analytical methodologies with the new Oracle Communications Data Model to deliver automated, simple, yet powerful predictive analytics solutions for customers.  Churn, sentiment analysis, identifying customer segments - all things that can be anticipated and hence, preconcieved and implemented inside an applications.  Read on for more information! TM Forum Management World, Nice, France - 18 May 2010 News Facts To help communications service providers (CSPs) manage and analyze rapidly growing data volumes cost effectively, Oracle today introduced the Oracle Communications Data Model. With the Oracle Communications Data Model, CSPs can achieve rapid time to value by quickly implementing a standards-based enterprise data warehouse that features communications industry-specific reporting, analytics and data mining. The combination of the Oracle Communications Data Model, Oracle Exadata and the Oracle Business Intelligence (BI) Foundation represents the most comprehensive data warehouse and BI solution for the communications industry. Also announced today, Hong Kong Broadband Network enhanced their data warehouse system, going live on Oracle Communications Data Model in three months. The leading provider increased its subscriber base by 37 percent in six months and reduced customer churn to less than one percent. Product Details Oracle Communications Data Model provides industry-specific schema and embedded analytics that address key areas such as customer management, marketing segmentation, product development and network health. CSPs can efficiently capture and monitor critical data and transform it into actionable information to support development and delivery of next-generation services using: More than 1,300 industry-specific measurements and key performance indicators (KPIs) such as network reliability statistics, provisioning metrics and customer churn propensity. Embedded OLAP cubes for extremely fast dimensional analysis of business information. Embedded data mining models for sophisticated trending and predictive analysis. Support for multiple lines of business, such as cable, mobile, wireline and Internet, which can be easily extended to support future requirements. With Oracle Communications Data Model, CSPs can jump start the implementation of a communications data warehouse in line with communications-industry standards including the TM Forum Information Framework (SID), formerly known as the Shared Information Model. Oracle Communications Data Model is optimized for any Oracle Database 11g platform, including Oracle Exadata, which can improve call data record query performance by 10x or more. Supporting Quotes "Oracle Communications Data Model covers a wide range of business areas that are relevant to modern communications service providers and is a comprehensive solution - with its data model and pre-packaged templates including BI dashboards, KPIs, OLAP cubes and mining models. It helps us save a great deal of time in building and implementing a customized data warehouse and enables us to leverage the advanced analytics quickly and more effectively," said Yasuki Hayashi, executive manager, NTT Comware Corporation. "Data volumes will only continue to grow as communications service providers expand next-generation networks, deploy new services and adopt new business models. They will increasingly need efficient, reliable data warehouses to capture key insights on data such as customer value, network value and churn probability. With the Oracle Communications Data Model, Oracle has demonstrated its commitment to meeting these needs by delivering data warehouse tools designed to fill communications industry-specific needs," said Elisabeth Rainge, program director, Network Software, IDC. "The TM Forum Conformance Mark provides reassurance to customers seeking standards-based, and therefore, cost-effective and flexible solutions. TM Forum is extremely pleased to work with Oracle to certify its Oracle Communications Data Model solution. Upon successful completion, this certification will represent the broadest and most complete implementation of the TM Forum Information Framework to date, with more than 130 aggregate business entities," said Keith Willetts, chairman and chief executive officer, TM Forum. Supporting Resources Oracle Communications Oracle Communications Data Model Data Sheet Oracle Communications Data Model Podcast Oracle Data Warehousing Oracle Communications on YouTube Oracle Communications on Delicious Oracle Communications on Facebook Oracle Communications on Twitter Oracle Communications on LinkedIn Oracle Database on Twitter The Data Warehouse Insider Blog

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• Where’s my MD.050?

  - by Dave Burke

  A question that I’m sometimes asked is “where’s my MD.050 in OUM?” For those not familiar with an MD.050, it serves the purpose of being a Functional Design Document (FDD) in one of Oracle’s legacy Methods. Functional Design Documents have existed for many years with their primary purpose being to describe the functional aspects of one or more components of an IT system, typically, a Custom Extension of some sort. So why don’t we have a direct replacement for the MD.050/FDD in OUM? In simple terms, the disadvantage of the MD.050/FDD approach is that it tends to lead practitioners into “Design mode” too early in the process. Whereas OUM encourages more emphasis on gathering, and describing the functional requirements of a system ahead of the formal Analysis and Design process. So that just means more work up front for the Business Analyst or Functional Consultants right? Well no…..the design of a solution, particularly when it involves a complex custom extension, does not necessarily take longer just because you put more thought into the functional requirements. In fact, one could argue the complete opposite, in that by putting more emphasis on clearly understanding the nuances of functionality requirements early in the process, then the overall time and cost incurred during the Analysis to Design process should be less. In short, as your understanding of requirements matures over time, it is far easier (and more cost effective) to update a document or a diagram, than to change lines of code. So how does that translate into Tasks and Work Products in OUM? Let us assume you have reached a point on a project where a Custom Extension is needed. One of the first things you should consider doing is creating a Use Case, and remember, a Use Case could be as simple as a few lines of text reflecting a “User Story”, or it could be what Cockburn1 describes a “fully dressed Use Case”. It is worth mentioned at this point the highly scalable nature of OUM in the sense that “documents” should not be produced just because that is the way we have always done things. Some projects may well be predicated upon a base of electronic documents, whilst other projects may take a much more Agile approach to describing functional requirements; through “User Stories” perhaps. In any event, it is quite common for a Custom Extension to involve the creation of several “components”, i.e. some new screens, an interface, a report etc. Therefore several Use Cases might be required, which in turn can then be assembled into a Use Case Package. Once you have the Use Cases attributed to an appropriate (fit-for-purpose) level of detail, and assembled into a Package, you can now create an Analysis Model for the Package. An Analysis Model is conceptual in nature, and depending on the solution being developing, would involve the creation of one or more diagrams (i.e. Sequence Diagrams, Collaboration Diagrams etc.) which collectively describe the Data, Behavior and Use Interface requirements of the solution. If required, the various elements of the Analysis Model may be indexed via an Analysis Specification. For Custom Extension projects that follow a pure Object Orientated approach, then the Analysis Model will naturally support the development of the Design Model without any further artifacts. However, for projects that are transitioning to this approach, then the various elements of the Analysis Model may be represented within the Analysis Specification. If we now return to the original question of “Where’s my MD.050”. The full answer would be: Capture the functional requirements within a Use Case Group related Use Cases into a Package Create an Analysis Model for each Package Consider creating an Analysis Specification (AN.100) as a index to each Analysis Model artifact An alternative answer for a relatively simple Custom Extension would be: Capture the functional requirements within a Use Case Optionally, group related Use Cases into a Package Create an Analysis Specification (AN.100) for each package 1 Cockburn, A, 2000, Writing Effective Use Case, Addison-Wesley Professional; Edition 1

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• Oracle BI and XS Energy Drinks – Don’t Miss the Amway Presentation!

  - by Maria Forney

  Amway is a global leader in the direct sales industry with $10.9B in annual sales in more than 100 countries and territories. The company has implemented a global BI framework that provides accurate, consistent, and timely insights to support global, regional and local analytical research, business planning, performance measurement and assessment. Oracle BI EE is used by 1500 employees across Amway sales, marketing, finance, and supply chain business units as well as Amway affiliates in Europe, Russia, South Africa, Japan, Australia, Latin America, Malaysia, Vietnam, and Indonesia. Last week, I spoke with Lead Data Analyst with Amway Global Sales, Dan Arganbright, and IT Manager with Amway BI Competency Center, Mike Olson, about their upcoming presentation at Oracle OpenWorld in San Francisco. Scheduled during a prime speaking slot on Monday, October 1 at 12:15pm in Moscone West, 2007, Dan and Mike will discuss their experience building Amway’s Distributor Consulting solution, powered by Oracle BI EE. You can find more information here. As background, Amway offers people an opportunity to own their own businesses and consumers exclusive products in health and wellness, beauty and home care.  The Amway internal Sales organization is charged with consulting leadership-level Distributors to help them with data insights and ultimately grow their business. Until recently, this was a resource-intense process of gathering and formatting data. In some markets, it took over 40 hours to collect the data and produce the analysis needed for one consultation session. Amway began its global BI journey in 2006 and since then the company has migrated from having multiple technology providers and integration points to an integrated strategic vendor approach. Today, the company has standardized on Oracle technology for BI.  Amway has achieved cost savings through the retirement of redundant technology platforms. In addition, Mike’s organization has led the charge to align disparate BI organizations into a BI Competency Center.  The following diagram highlights the simplicity of the standardized architecture of Amway today. Dubbed Distributor Consulting, Amway has developed a BI solution using the Oracle technology stack to help Distributor leaders grow their businesses. The Distributor Consulting solution provides over 40 metrics for Sales staff to provide data-driven insights on the Distributors and organizations they support.  Using Oracle BI EE, Exadata, and Oracle Data Integrator, Amway provides customized and personalized business intelligence, and the Oracle BI EE dashboards were developed by the Amway Sales organization, which demonstrates business empowerment of the technology. Amway is also leveraging the power of BI to drive business growth in all of its markets.  A new set of Distributor Segmentation metrics are enabling a better understanding of distributor behaviors. A Global Scorecard that Amway developed provides key metrics at a market and global level for executive-level discussions. Product Analysis teams can now highlight repeat purchase rates, product penetration and the success of CRM campaigns. In the words of Dan and Mike, the addition of Exadata 11 months ago has been “a game changer.”  Amway has been able to dramatically reduce complexity, improve performance and increase business productivity and cost savings. For example, the number of indexes on the global data warehouse was reduced from more than 1,000 to less than 20.  Pulling data for the highest level distributors or the largest markets in the company now can be done in minutes instead of hours.  As a result, IT has shifted from performance tuning and keeping the system operational to higher-value business-focused activities. •       “The distributors that have been introduced to the BI reports have found them extremely helpful. Because they have never had this kind of information before, when they were presented with the reports, they wanted to take action immediately!”  -     Sales Development Manager in Latin America Without giving away more, the Amway case study presentation will be one of the unique customer sessions at OpenWorld this year. Speakers Dan Arganbright and Mike Olson have planned an interactive and entertaining session on Monday October 1 at 12:15pm in Moscone West, 2007. I’ll see you there!

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• Oracle BI and XS Energy Drinks – Don’t Miss the Amway Presentation!

  - by Michelle Kimihira

  By Maria Forney Amway is a global leader in the direct sales industry with $10.9B in annual sales in more than 100 countries and territories. The company has implemented a global BI framework that provides accurate, consistent, and timely insights to support global, regional and local analytical research, business planning, performance measurement and assessment. Oracle BI EE is used by 1500 employees across Amway sales, marketing, finance, and supply chain business units as well as Amway affiliates in Europe, Russia, South Africa, Japan, Australia, Latin America, Malaysia, Vietnam, and Indonesia. Last week, I spoke with Lead Data Analyst with Amway Global Sales, Dan Arganbright, and IT Manager with Amway BI Competency Center, Mike Olson, about their upcoming presentation at Oracle OpenWorld in San Francisco. Scheduled during a prime speaking slot on Monday, October 1 at 12:15pm in Moscone West, 2007, Dan and Mike will discuss their experience building Amway’s Distributor Consulting solution, powered by Oracle BI EE. You can find more information here. As background, Amway offers people an opportunity to own their own businesses and consumers exclusive products in health and wellness, beauty and home care.  The Amway internal Sales organization is charged with consulting leadership-level Distributors to help them with data insights and ultimately grow their business. Until recently, this was a resource-intense process of gathering and formatting data. In some markets, it took over 40 hours to collect the data and produce the analysis needed for one consultation session. Amway began its global BI journey in 2006 and since then the company has migrated from having multiple technology providers and integration points to an integrated strategic vendor approach. Today, the company has standardized on Oracle technology for BI.  Amway has achieved cost savings through the retirement of redundant technology platforms. In addition, Mike’s organization has led the charge to align disparate BI organizations into a BI Competency Center.  The following diagram highlights the simplicity of the standardized architecture of Amway today. Dubbed Distributor Consulting, Amway has developed a BI solution using the Oracle technology stack to help Distributor leaders grow their businesses. The Distributor Consulting solution provides over 40 metrics for Sales staff to provide data-driven insights on the Distributors and organizations they support.  Using Oracle BI EE, Exadata, and Oracle Data Integrator, Amway provides customized and personalized business intelligence, and the Oracle BI EE dashboards were developed by the Amway Sales organization, which demonstrates business empowerment of the technology. Amway is also leveraging the power of BI to drive business growth in all of its markets.  A new set of Distributor Segmentation metrics are enabling a better understanding of distributor behaviors. A Global Scorecard that Amway developed provides key metrics at a market and global level for executive-level discussions. Product Analysis teams can now highlight repeat purchase rates, product penetration and the success of CRM campaigns. In the words of Dan and Mike, the addition of Exadata 11 months ago has been “a game changer.”  Amway has been able to dramatically reduce complexity, improve performance and increase business productivity and cost savings. For example, the number of indexes on the global data warehouse was reduced from more than 1,000 to less than 20.  Pulling data for the highest level distributors or the largest markets in the company now can be done in minutes instead of hours.  As a result, IT has shifted from performance tuning and keeping the system operational to higher-value business-focused activities. •       “The distributors that have been introduced to the BI reports have found them extremely helpful. Because they have never had this kind of information before, when they were presented with the reports, they wanted to take action immediately!”  -     Sales Development Manager in Latin America Without giving away more, the Amway case study presentation will be one of the unique customer sessions at OpenWorld this year. Speakers Dan Arganbright and Mike Olson have planned an interactive and entertaining session on Monday October 1 at 12:15pm in Moscone West, 2007. I’ll see you there!

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• Talking JavaOne with Rock Star Simon Ritter

  - by Janice J. Heiss

  Oracle’s Java Technology Evangelist Simon Ritter is well known at JavaOne for his quirky and fun-loving sessions, which, this year include: CON4644 -- “JavaFX Extreme GUI Makeover” (with Angela Caicedo on how to improve UIs in JavaFX) CON5352 -- “Building JavaFX Interfaces for the Real World” (Kinect gesture tracking and mind reading) CON5348 -- “Do You Like Coffee with Your Dessert?” (Some cool demos of Java of the Raspberry Pi) CON6375 -- “Custom JavaFX Charts: (How to extend JavaFX Chart controls with some interesting things) I recently asked Ritter about the significance of the Raspberry Pi, the topic of one of his sessions that consists of a credit card-sized single-board computer developed in the UK with the intention of stimulating the teaching of basic computer science in schools. “I don't think there's one definitive thing that makes the RP significant,” observed Ritter, “but a combination of things that really makes it stand out. First, it's the cost: $35 for what is effectively a completely usable computer. OK, so you have to add a power supply, SD card for storage and maybe a screen, keyboard and mouse, but this is still way cheaper than a typical PC. The choice of an ARM processor is also significant, as it avoids problems like cooling (no heat sink or fan) and can use a USB power brick.  Combine these two things with the immense groundswell of community support and it provides a fantastic platform for teaching young and old alike about computing, which is the real goal of the project.”He informed me that he’ll be at the Raspberry Pi meetup on Saturday (not part of JavaOne). Check out the details here.JavaFX InterfacesWhen I asked about how JavaFX can interface with the real world, he said that there are many ways. “JavaFX provides you with a simple set of programming interfaces that can create complex, cool and compelling user interfaces,” explained Ritter. “Because it's just Java code you can combine JavaFX with any other Java library to provide data to display and control the interface. What I've done for my session is look at some of the possible ways of doing this using some of the amazing hardware that's available today at very low cost. The Kinect sensor has added a new dimension to gaming in terms of interaction; there's a Java API to access this so you can easily collect skeleton tracking data from it. Some clever people have also written libraries that can track gestures like swipes, circles, pushes, and so on. We use these to control parts of the UI. I've also experimented with a Neurosky EEG sensor that can in some ways ‘read your mind’ (well, at least measure some of the brain functions like attention and meditation).  I've written a Java library for this that I include as a way of controlling the UI. We're not quite at the stage of just thinking a command though!” Here Comes Java EmbeddedAnd what, from Ritter’s perspective, is the most exciting thing happening in the world of Java today? “I think it's seeing just how Java continues to become more and more pervasive,” he said. “One of the areas that is growing rapidly is embedded systems.  We've talked about the ‘Internet of things’ for many years; now it's finally becoming a reality. With the ability of more and more devices to include processing, storage and networking we need an easy way to write code for them that's reliable, has high performance, and is secure. Java fits all these requirements. With Java Embedded being a conference within a conference, I'm very excited about the possibilities of Java in this space.”Check out Ritter’s sessions or say hi if you run into him. Originally published on blogs.oracle.com/javaone.

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• Talking JavaOne with Rock Star Simon Ritter

  - by Janice J. Heiss

  Oracle’s Java Technology Evangelist Simon Ritter is well known at JavaOne for his quirky and fun-loving sessions, which, this year include: CON4644 -- “JavaFX Extreme GUI Makeover” (with Angela Caicedo on how to improve UIs in JavaFX) CON5352 -- “Building JavaFX Interfaces for the Real World” (Kinect gesture tracking and mind reading) CON5348 -- “Do You Like Coffee with Your Dessert?” (Some cool demos of Java of the Raspberry Pi) CON6375 -- “Custom JavaFX Charts: (How to extend JavaFX Chart controls with some interesting things) I recently asked Ritter about the significance of the Raspberry Pi, the topic of one of his sessions that consists of a credit card-sized single-board computer developed in the UK with the intention of stimulating the teaching of basic computer science in schools. “I don't think there's one definitive thing that makes the RP significant,” observed Ritter, “but a combination of things that really makes it stand out. First, it's the cost: $35 for what is effectively a completely usable computer. OK, so you have to add a power supply, SD card for storage and maybe a screen, keyboard and mouse, but this is still way cheaper than a typical PC. The choice of an ARM processor is also significant, as it avoids problems like cooling (no heat sink or fan) and can use a USB power brick.  Combine these two things with the immense groundswell of community support and it provides a fantastic platform for teaching young and old alike about computing, which is the real goal of the project.”He informed me that he’ll be at the Raspberry Pi meetup on Saturday (not part of JavaOne). Check out the details here.JavaFX InterfacesWhen I asked about how JavaFX can interface with the real world, he said that there are many ways. “JavaFX provides you with a simple set of programming interfaces that can create complex, cool and compelling user interfaces,” explained Ritter. “Because it's just Java code you can combine JavaFX with any other Java library to provide data to display and control the interface. What I've done for my session is look at some of the possible ways of doing this using some of the amazing hardware that's available today at very low cost. The Kinect sensor has added a new dimension to gaming in terms of interaction; there's a Java API to access this so you can easily collect skeleton tracking data from it. Some clever people have also written libraries that can track gestures like swipes, circles, pushes, and so on. We use these to control parts of the UI. I've also experimented with a Neurosky EEG sensor that can in some ways ‘read your mind’ (well, at least measure some of the brain functions like attention and meditation).  I've written a Java library for this that I include as a way of controlling the UI. We're not quite at the stage of just thinking a command though!” Here Comes Java EmbeddedAnd what, from Ritter’s perspective, is the most exciting thing happening in the world of Java today? “I think it's seeing just how Java continues to become more and more pervasive,” he said. “One of the areas that is growing rapidly is embedded systems.  We've talked about the ‘Internet of things’ for many years; now it's finally becoming a reality. With the ability of more and more devices to include processing, storage and networking we need an easy way to write code for them that's reliable, has high performance, and is secure. Java fits all these requirements. With Java Embedded being a conference within a conference, I'm very excited about the possibilities of Java in this space.”Check out Ritter’s sessions or say hi if you run into him.

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• Why Cornell University Chose Oracle Data Masking

  - by Troy Kitch

  One of the eight Ivy League schools, Cornell University found itself in the unfortunate position of having to inform over 45,000 University community members that their personal information had been breached when a laptop was stolen. To ensure this wouldn’t happen again, Cornell took steps to ensure that data used for non-production purposes is de-identified with Oracle Data Masking. A recent podcast highlights why organizations like Cornell are choosing Oracle Data Masking to irreversibly de-identify production data for use in non-production environments. Organizations often copy production data, that contains sensitive information, into non-production environments so they can test applications and systems using “real world” information. Data in non-production has increasingly become a target of cyber criminals and can be lost or stolen due to weak security controls and unmonitored access. Similar to production environments, data breaches in non-production environments can cost millions of dollars to remediate and cause irreparable harm to reputation and brand. Cornell’s applications and databases help carry out the administrative and academic mission of the university. They are running Oracle PeopleSoft Campus Solutions that include highly sensitive faculty, student, alumni, and prospective student data. This data is supported and accessed by a diverse set of developers and functional staff distributed across the university. Several years ago, Cornell experienced a data breach when an employee’s laptop was stolen.  Centrally stored backup information indicated there was sensitive data on the laptop. With no way of knowing what the criminal intended, the university had to spend significant resources reviewing data, setting up service centers to handle constituent concerns, and provide free credit checks and identity theft protection services—all of which cost money and took time away from other projects. To avoid this issue in the future Cornell came up with several options; one of which was to sanitize the testing and training environments. “The project management team was brought in and they developed a project plan and implementation schedule; part of which was to evaluate competing products in the market-space and figure out which one would work best for us.  In the end we chose Oracle’s solution based on its architecture and its functionality.” – Tony Damiani, Database Administration and Business Intelligence, Cornell University The key goals of the project were to mask the elements that were identifiable as sensitive in a consistent and efficient manner, but still support all the previous activities in the non-production environments. Tony concludes,  “What we saw was a very minimal impact on performance. The masking process added an additional three hours to our refresh window, but it was well worth that time to secure the environment and remove the sensitive data. I think some other key points you can keep in mind here is that there was zero impact on the production environment. Oracle Data Masking works in non-production environments only. Additionally, the risk of exposure has been significantly reduced and the impact to business was minimal.” With Oracle Data Masking organizations like Cornell can: Make application data securely available in non-production environments Prevent application developers and testers from seeing production data Use an extensible template library and policies for data masking automation Gain the benefits of referential integrity so that applications continue to work Listen to the podcast to hear the complete interview.  Learn more about Oracle Data Masking by registering to watch this SANS Institute Webcast and view this short demo.

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• SQL SERVER – CXPACKET – Parallelism – Advanced Solution – Wait Type – Day 7 of 28

  - by pinaldave

  Earlier we discussed about the what is the common solution to solve the issue with CXPACKET wait time. Today I am going to talk about few of the other suggestions which can help to reduce the CXPACKET wait. If you are going to suggest that I should focus on MAXDOP and COST THRESHOLD – I totally agree. I have covered them in details in yesterday’s blog post. Today we are going to discuss few other way CXPACKET can be reduced. Potential Reasons: If data is heavily skewed, there are chances that query optimizer may estimate the correct amount of the data leading to assign fewer thread to query. This can easily lead to uneven workload on threads and may create CXPAKCET wait. While retrieving the data one of the thread face IO, Memory or CPU bottleneck and have to wait to get those resources to execute its tasks, may create CXPACKET wait as well. Data which is retrieved is on different speed IO Subsystem. (This is not common and hardly possible but there are chances). Higher fragmentations in some area of the table can lead less data per page. This may lead to CXPACKET wait. As I said the reasons here mentioned are not the major cause of the CXPACKET wait but any kind of scenario can create the probable wait time. Best Practices to Reduce CXPACKET wait: Refer earlier article regarding MAXDOP and Cost Threshold. De-fragmentation of Index can help as more data can be obtained per page. (Assuming close to 100 fill-factor) If data is on multiple files which are on multiple similar speed physical drive, the CXPACKET wait may reduce. Keep the statistics updated, as this will give better estimate to query optimizer when assigning threads and dividing the data among available threads. Updating statistics can significantly improve the strength of the query optimizer to render proper execution plan. This may overall affect the parallelism process in positive way. Bad Practice: In one of the recent consultancy project, when I was called in I noticed that one of the ‘experienced’ DBA noticed higher CXPACKET wait and to reduce them, he has increased the worker threads. The reality was increasing worker thread has lead to many other issues. With more number of the threads, more amount of memory was used leading memory pressure. As there were more threads CPU scheduler faced higher ‘Context Switching’ leading further degrading performance. When I explained all these to ‘experienced’ DBA he suggested that now we should reduce the number of threads. Not really! Lower number of the threads may create heavy stalling for parallel queries. I suggest NOT to touch the setting of number of the threads when dealing with CXPACKET wait. Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and I no way claim it to be accurate. I suggest reading book on-line for further clarification. All the discussion of Wait Stats over here is generic and it varies by system to system. You are recommended to test this on development server before implementing to production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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• Craig Mundie's video

  - by GGBlogger

  Timothy recently posted “Microsoft Shows Off Radical New UI, Could Be Used In Windows 8” on Slashdot. I took such grave exception to his post that I found it necessary to my senses to write this blog. We need to go back many years to the days of hand cranked calculators and early main frame computers. These devices had singular purposes – they were “number crunchers” used to make accounting easier. The front facing display in early mainframes was “blinken lights.” The calculators did provide printing – in the form of paper tape and the mainframes used line printers to generate reports as needed. We had other metaphors to work with. The typewriter was/is a mechanical device that substitutes for a type setting machine. The originals go back to 1867 and the keyboard layout has remained much the same to this day. In the earlier years the Morse code telegraphs gave way to Teletype machines. The old ASR33, seen on the left in this photo of one of the first computers I help manufacture, used a keyboard very similar to the keyboards in use today. It also generated punched paper tape that we generated to program this computer in machine language. Everything considered this computer which dates back to the late 1960s has a keyboard for input and a roll of paper as output. So in a very rudimentary fashion little has changed. Oh – we didn’t have a mouse! The entire point of this exercise is to point out that we still use very similar methods to get data into and out of a computer regardless of the operating system involved. The Altair, IMSAI, Apple, Commodore and onward to our modern machines changed the hardware that we interfaced to but changed little in the way we input, view and output the results of our computing effort. The mouse made some changes and the advent of windowed interfaces such as Windows and Apple made things somewhat easier for the user. My 4 year old granddaughter plays here Dora games on our computer. She knows how to start programs, use the mouse, play the game and is quite adept so we have come some distance in making computers useable. One of my chief bitches is the constant harangues leveled at Microsoft. Yup – they are a money making organization. You like Apple? No problem for me. I don’t use Apple mostly because I’m comfortable in the Windows environment but probably more because I don’t like Apple’s “Holier than thou” attitude. Some think they do superior things and that’s also fine with me. Obviously the iPhone has not done badly and other Apple products have fared well. But they are expensive. I just build a new machine with 4 Terabytes of storage, an Intel i7 Core 950 processor and 12 GB of RAMIII. It cost me – with dual monitors – less than 2000 dollars. Now to the chief reason for this blog. I’m going to continue developing software for as long as I’m able. For that reason I don’t see my keyboard, mouse and displays changing much for many years. I also don’t think Microsoft is going to spoil that for me by making radical changes to my developer experience. What Craig Mundie does in his video here:  http://www.ispyce.com/2011/02/microsoft-shows-off-radical-new-ui.html is explore the potential future of computer interfaces for the masses of potential users. Using a computer today requires a person to have rudimentary capabilities with keyboards and the mouse. Wouldn’t it be great if all they needed was hand gestures? Although not mentioned it would also be nice if computers responded intelligently to a user’s voice. There is absolutely no argument with the fact that user interaction with these machines is going to change over time. My personal prediction is that it will take years for much of what Craig discusses to come to a cost effective reality but it is certainly coming. I just don’t believe that what Craig discusses will be the future look of a Window 8.

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• The Legend of the Filtered Index

  - by Johnm

  Once upon a time there was a big and bulky twenty-nine million row table. He tempestuously hoarded data like a maddened shopper amid a clearance sale. Despite his leviathan nature and eager appetite he loved to share his treasures. Multitudes from all around would embark upon an epiphanous journey to sample contents of his mythical purse of knowledge. After a long day of performing countless table scans the table was overcome with fatigue. After a short period of unavailability, he decided that he needed to consider a new way to share his prized possessions in a more efficient manner. Thus, a non-clustered index was born. She dutifully directed the pilgrims that sought the table's data - no longer would those despicable table scans darken the doorsteps of this quaint village. and yet, the table's veracious appetite did not wane. Any bit or byte that wondered near him was consumed with vigor. His columns and rows continued to expand beyond the expectations of even the most liberal estimation. As his rows grew grander they became more difficult to organize and maintain. The once bright and cheerful disposition of the non-clustered index began to dim. The wait time for those who sought the table's treasures began to increase. Some of those who came to nibble upon the banquet of knowledge even timed-out and never realized their aspired enlightenment. After a period of heart-wrenching introspection, the table decided to drop the index and attempt another solution. At the darkest hour of the table's desperation came a grand flash of light. As his eyes regained their vision there stood several creatures who looked very similar to his former, beloved, non-clustered index. They all spoke in unison as they introduced themselves: "Fear not, for we come to organize your data and direct those who seek to partake in it. We are the filtered index." Immediately, the filtered indexes began to scurry about. One took control of the past quarter's data. Another took control of the previous quarter's data. All of the remaining filtered indexes followed suit. As the nearly gluttonous habits of the table scaled forward more filtered indexes appeared. Regardless of the table's size, all of the eagerly awaiting data seekers were delivered data as quickly as a Jimmy John's sandwich. The table was moved to tears. All in the land of data rejoiced and all lived happily ever after, at least until the next data challenge crept from the fearsome cave of the unknown. The End.

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