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In September 2018, IBM introduced a brand recent product, IBM Db2 AI for z/OS. This ersatz intelligence engine monitors records entry patterns from executing SQL statements, makes exercise of desktop getting to know algorithms to pick out most efficient patterns and passes this counsel to the Db2 query optimizer to live used by means of subsequent statements.machine discovering on the IBM z Platform
In may furthermore of 2018, IBM announced version 1.2 of its computer researching for z/OS (MLz) product. this is a hybrid zServer and cloud software suite that ingests performance records, analyzes and builds models that characterize the health fame of quite a lot of symptoms, screens them over time and provides precise-time scoring capabilities.
a couple of elements of this product providing are aimed at helping a community of model developers and managers. for instance:
This computer gaining learning of suite changed into at first aimed toward zServer-based mostly analytics functions. one of the vital first glaring decisions become zSystem performance monitoring and tuning. system administration Facility (SMF) records which are immediately generated by using the working system provide the uncooked facts for paraphernalia aid consumption akin to central processor utilization, I/O processing, reminiscence paging and the like. IBM MLz can assemble and shop these records over time, and construct and coach models of system conduct, ranking these behaviors, identify patterns not with ease foreseen by using people, develop key performance symptoms (KPIs) after which feed the mannequin effects lower back into the device to occupy an effect on paraphernalia configuration adjustments that may extend performance.
The subsequent step become to implement this suite to investigate Db2 efficiency records. One solution, called the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the laptop learning know-how to Db2 operational information to benefit an understanding of Db2 subsystem fitness. it might dynamically build baselines for key efficiency indications, deliver a dashboard of those KPIs and give operational workforce precise-time insight into Db2 operations.
while time-honored Db2 subsystem performance is an vital constituent in everyday software health and efficiency, IBM estimates that the DBA serve staff spends 25% or greater of its time, " ... combating entry direction complications which antecedent efficiency degradation and repair affect.". (See Reference 1).AI involves Db2
consider the plight of modern DBAs in a Db2 ambiance. In today's IT world they must assist one or extra massive records functions, cloud software and database functions, application installation and configuration, Db2 subsystem and application performance tuning, database definition and administration, calamity recuperation planning, and extra. question tuning has been in being on the grounds that the origins of the database, and DBAs are always tasked with this as well.
The heart of query route analysis in Db2 is the Optimizer. It accepts SQL statements from purposes, verifies authority to access the statistics, studies the areas of the objects to live accessed and develops a list of candidate statistics access paths. These access paths can encompass indexes, desk scans, a lot of desk live a allotment of methods and others. within the information warehouse and large facts environments there are always additional selections available. One of these is the being of summary tables (sometimes called materialized question tables) that contain pre-summarized or aggregated information, accordingly enabling Db2 to preserve away from re-aggregation processing. yet another selection is the starjoin access route, measure within the statistics warehouse, the plot the order of desk joins is modified for efficiency motives.
The Optimizer then experiences the candidate access paths and chooses the access route, "with the lowest cost." can saturate in this context capacity a weighted summation of useful resource utilization including CPU, I/O, reminiscence and different components. eventually, the Optimizer takes the bottom cost access direction, stores it in reminiscence (and, optionally, in the Db2 listing) and starts off access route execution.
large records and information warehouse operations now comprehend application suites that allow the business analyst to accomplish exercise of a graphical interface to build and maneuver a miniature data mannequin of the records they want to analyze. The programs then generate SQL statements according to the users’ requests.
The hardship for the DBA
so as to conclude excellent analytics for your multiple facts shops you exigency an outstanding understanding of the records requirements, an figuring out of the analytical services and algorithms accessible and a excessive-performance information infrastructure. sadly, the number and location of records sources is increasing (each in size and in geography), statistics sizes are starting to be, and applications proceed to proliferate in number and complexity. How should IT managers aid this atmosphere, chiefly with essentially the most experienced and ripen staff nearing retirement?
consider additionally that a huge allotment of reducing the full saturate of possession of those systems is to glean Db2 functions to flee quicker and greater efficiently. This always interprets into using fewer CPU cycles, doing fewer I/Os and transporting less information across the network. given that it's commonly difficult to even identify which functions could benefit from efficiency tuning, one approach is to automate the detection and correction of tuning issues. here is the plot laptop gaining learning of and synthetic intelligence may furthermore live used to tremendous impact.Db2 12 for z/OS and synthetic Intelligence
Db2 edition 12 on z/OS uses the desktop learning facilities mentioned above to acquire and save SQL query textual content and access route particulars, in addition to precise efficiency-linked weak suggestions akin to CPU time used, elapsed instances and effect set sizes. This providing, described as Db2 AI for z/OS, analyzes and shops the facts in machine learning fashions, with the model analysis outcomes then being scored and made attainable to the Db2 Optimizer. The subsequent time a scored SQL commentary is encountered, the Optimizer can then exercise the model scoring statistics as input to its entry path selection algorithm.
The result may quiet live a reduction in CPU consumption because the Optimizer uses mannequin scoring enter to choose more suitable entry paths. This then lowers CPU prices and speeds application response times. a major competencies is that the usage of AI software doesn't require the DBA to occupy statistics science talents or profound insights into query tuning methodologies. The Optimizer now chooses the most advantageous access paths based now not simplest on SQL query syntax and facts distribution records however on modelled and scored weak efficiency.
This may furthermore live specifically captious if you store records in diverse locations. as an instance, many analytical queries against large statistics require concurrent entry to unavoidable information warehouse tables. These tables are generally known as dimension tables, and they contain the records points usually used to manage subsetting and aggregation. as an instance, in a retail atmosphere believe a table known as StoreLocation that enumerates each save and its vicinity code. Queries against save income information may additionally exigency to aggregate or summarize income by using vicinity; therefore, the StoreLocation table should live used by course of some large data queries. in this atmosphere it's accustomed to consume the dimension tables and copy them continuously to the large facts application. within the IBM world this region is the IBM Db2 Analytics Accelerator (IDAA).
Now believe about SQL queries from each operational applications, facts warehouse users and sizable records enterprise analysts. From Db2's viewpoint, every bit of these queries are equal, and are forwarded to the Optimizer. youngsters, within the case of operational queries and warehouse queries they should quiet definitely live directed to entry the StoreLocation table in the warehouse. even so, the question from the business analyst against large facts tables should doubtless entry the reproduction of the desk there. This outcomes in a proliferations of learning entry paths, and extra drudgery for the Optimizer. fortunately, Db2 AI for z/OS can supply the Optimizer the suggestions it needs to accomplish wise entry route selections.the course it Works
The sequence of pursuits in Db2 AI for z/OS (See Reference 2) is frequently privilege here:
There are furthermore a considerable number of user interfaces that give the administrator visibility to the popularity of the amassed SQL remark performance records and mannequin scoring.summary
IBM's machine getting to know for zOS (MLz) providing is getting used to powerful effect in Db2 edition 12 to enhance the efficiency of analytical queries as well as operational queries and their linked functions. This requires administration attention, as you occupy to investigate that your enterprise is ready to consume these ML and AI conclusions. How will you measure the costs and benefits of the usage of machine researching? Which IT assist workforce should live tasked to reviewing the outcomes of mannequin scoring, and maybe approving (or overriding) the effects? How will you assessment and warrant the assumptions that the application makes about entry path choices?
In other words, how neatly conclude you know your statistics, its distribution, its integrity and your latest and proposed entry paths? this could investigate where the DBAs disburse their time in helping analytics and operational utility performance.
# # #
John Campbell, IBM Db2 special EngineerFrom "IBM Db2 AI for z/OS: enhance IBM Db2 utility performance with desktop researching"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-boost-ibm-db2-utility-efficiency-with-ma
Db2 AI for z/OShttps://www.ibm.com/aid/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
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Ricardo Balduino and Tim BohnEarly Flight, Creative Commons Introduction
As they described in allotment 1 of this series, their objective is to serve prognosticate the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They exercise historical flights data and historical weather data to accomplish predictions for upcoming flights.
Over the course of this four-part series, they exercise different platforms to serve us with those predictions. Here in allotment 2, they exercise the IBM SPSS Modeler and APIs from The Weather Company.Tools used in this exercise case solution
IBM SPSS Modeler is designed to serve determine patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a orbit of advanced algorithms and analysis techniques, including text analytics, entity analytics, determination management and optimization to deliver insights in near real-time. For this exercise case, they used SPSS Modeler 18.1 to create a visual representation of the solution, or in SPSS terms, a stream. That’s right — not one line of code was written in the making of this blog.
We furthermore used The Weather Company APIs to retrieve historical weather data for the ten airports over the year 2016. IBM SPSS Modeler supports calling the weather APIs from within a stream. That is accomplished by adding extensions to SPSS, available in the IBM SPSS Predictive Analytics resources page, a.k.a. Extensions Hub.A proposed solution
In this blog, they propose one workable solution for this problem. It’s not meant to live the only or the best workable solution, or a production-level solution for that matter, but the discussion presented here covers the typical iterative process (described in the sections below) that helps us accumulate insights and refine the predictive model across iterations. They inspirit the readers to try and approach up with different solutions, and provide us with your feedback for future blogs.Business and data understanding
The first step of the iterative process includes understanding and gathering the data needed to train and test their model later.
Flights data — We gathered 2016 flights data from the US Bureau of Transportation Statistics website. The website allows us to export one month at a time, so they ended up with 12 csv (comma separated value) files. They used IBM SPSS Modeler to merge every bit of the csv files into one set and to select the ten airports in their scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in device 1.Figure 1 — gathering and preparing flights data in IBM SPSS Modeler
Weather data — From the Extensions Hub, they added the TWCHistoricalGridded extension to SPSS Modeler, which made the extension available as a node in the tool. That node took a csv file listing the 10 airports latitude and longitude coordinates as input, and generated the historical hourly data for the entire year of 2016, for each airport location, as seen in device 2.Figure 2 — gathering and preparing weather data in IBM SPSS Modeler
Combined flights and weather data — To each flight in the first data set, they added two recent columns: root and DEST, containing the respective airport codes. Next, flight data and the weather data were merged together. Note: the “stars” or SPSS super nodes in device 3 are placeholders for the diagrams in Figures 1 and 2 above.Figure 3 — combining flights and weather data in IBM SPSS Modeler Data preparation, modeling, and evaluation
We iteratively performed the following steps until the desired model qualities were reached:
· Prepare data
· perform modeling
· Evaluate the model
Figure 4 shows the first and second iterations of their process in IBM SPSS Modeler.Figure 4 — iterations: prepare data, flee models, evaluate — and conclude it again First iteration
To start preparing the data, they used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took custody of null values). In order to better train the model later on, they filtered out rows where flight cancellations were not related to weather conditions (e.g. cancellations due to technical issues, security issues, etc.)Figure 5 — imbalanced data establish in their input data set
This is an lively exercise case, and often a hard one to solve, due to the imbalanced data it presents, as seen in device 5. By “imbalanced” they weigh in that there were far more non-cancelled flights in the historical data than cancelled ones. They will dispute how they dealt with imbalanced data in the following iteration.
Next, they defined which features were required as inputs to the model (such as flight date, hour, day of the week, root and destination airport codes, and weather conditions), and which one was the target to live generated by the model (i.e. prognosticate the cancellation status). They then partitioned the data into training and testing sets, using an 85/15 ratio.
The partitioned data was fed into an SPSS node called Auto Classifier. This node allowed us to flee multiple models at once and preview their outputs, such as the region under the ROC curve, as seen in device 6.Figure 6 — models output provided by the Auto Classifier node
That was a useful step in making an initial selection of a model for further refinement during subsequent iterations. They decided to exercise the Random Trees model since the initial analysis showed it has the best region under the curve as compared to the other models in the list.Second iteration
During the second iteration, they addressed the skewedness of the original data. For that purpose, they chose one of the SPSS nodes called SMOTE (Synthetic Minority Over-sampling Technique). This node provides an advanced over-sampling algorithm that deals with imbalanced datasets, which helped their selected model drudgery more effectively.Figure 7 — distribution of cancelled and non-cancelled flights after using SMOTE
In device 7, they notice a more balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.
As mentioned earlier, they picked the Random Trees model for this sample solution. This SPSS node provides a model for tree-based classification and prediction that is built on Classification and Regression Tree methodology. Due to its characteristics, this model is much less recumbent to overfitting, which gives a higher likelihood of repeating the same test results when you exercise recent data, that is, data that was not allotment of the original training and testing data sets. Another advantage of this method — in particular for their exercise case — is its faculty to maneuver imbalanced data.
Since in this exercise case they are dealing with classification analysis, they used two common ways to evaluate the performance of the model: confusion matrix and ROC curve. One of the outputs of running the Random Trees model in SPSS is the confusion matrix seen in device 8. The table shows the precision achieved by the model during training.Figure 8 — Confusion Matrix for cancelled vs. non-cancelled flights
In this case, the model’s precision was about 95% for predicting cancelled flights (true positives), and about 94% for predicting non-cancelled flights (true negatives). That means, the model was correct most of the time, but furthermore made wrong predictions about 4–5% of the time (false negatives and wrong positives).
That was the precision given by the model using the training data set. This is furthermore represented by the ROC curve on the left side of device 9. They can see, however, that the region under the curve for the training data set was better than the region under the curve for the testing data set (right side of device 9), which means that during testing, the model did not perform as well as during training (i.e. it presented a higher rate of errors, or higher rate of wrong negatives and wrong positives).Figure 9 — ROC curves for the training and testing data sets
Nevertheless, they decided that the results were quiet excellent for the purposes of their discussion in this blog, and they stopped their iterations here. They inspirit readers to further refine this model or even to exercise other models that could decipher this exercise case.Deploying the model
Finally, they deployed the model as a comfort API that developers can summon from their applications. For that, they created a “deployment branch” in the SPSS stream. Then, they used the IBM Watson Machine Learning service available on IBM Bluemix here. They imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can furthermore summon The Weather Company APIs directly from their application code to retrieve the forecast data for the next day, week, and so on, in order to pass the required data to the scoring endpoint and accomplish the prediction.
A typical scoring endpoint provided by the Watson Machine Learning service would stare relish the URL shown below.
https://ibm-watson-ml.mybluemix.net/pm/v1/score/flights-cancellation?accesskey=<provided by WML service>
By passing the expected JSON carcass that includes the required inputs for scoring (such as the future flight data and forecast weather data), the scoring endpoint above returns if a given flight is likely to live cancelled or not. This is seen in device 10, which shows a summon being made to the scoring endpoint — and its response — using an HTTP requester appliance available in a web browser.Figure 10 — actual request URL, JSON body, and response from scoring endpoint
Notice in the JSON response above that the deployed model predicted this particular flight from Newark to Chicago would live 88.8% likely to live cancelled, based on forecast weather conditions.Conclusion
IBM SPSS Modeler is a powerful appliance that helped us visually create a solution for this exercise case without writing a single line of code. They were able to ensue an iterative process that helped us understand and prepare the data, then model and evaluate the solution, to finally deploy the model as an API for consumption by application developers.Resources
The IBM SPSS stream and data used as the basis for this blog are available on GitHub. There you can furthermore find instructions on how to download IBM SPSS Modeler, glean a key for The Weather Channel APIs, and much more.
Royalty-free I3C; CFET parasitic variation modeling; Intel funds analog IP generation.
The MIPI Alliance released MIPI I3C Basic v1.0, a subset of the MIPI I3C sensor interface specification that bundles 20 of the most commonly needed I3C features for developers and other standards organizations. The royalty-free specification includes backward compatibility with I2C, 12.5 MHz multi-drop bus that is over 12 times faster than I2C supports, in-band interrupts to allow slaves to notify masters of interrupts, dynamic address assignment, and standardized discovery.
Efinix will expand its product offering, adding a 200K logic constituent FPGA to its lineup with the Triton T200. The T200 targets AI-driven products, and its architecture has enough LEs, DSP blocks, and on-chip RAM to deliver 1 TOPS for CNN at INT8 precision and 5 TOPS for BNN, according to Efinix CEO Sammy Cheung. The company furthermore released samples of its Trion T20 FPGA.
Faraday Technology released multi-protocol video interface IP on UMC 28nm HPC. The Multi-Protocol Video Interface IP solution supports both transmitter (TX) and receiver (RX). The transmitter allows for MIPI and CMOS-IO combo solutions for package cost reduction and flexibility, while the receiver combo PHY includes MIPI, LVDS, subLVDS, HiSPi, and CMOS-I/O to support a diversified orbit of interfaces to CMOS image sensors. Target applications comprehend panel and sensor interfaces, projectors, MFP, DSC, surveillance, AR and VR, and AI.
Analog appliance and IP maker Movellus closed a second round of funding from Intel Capital. Movellus’ technology automatically generates analog IPs using digital implementation tools and measure cells. The company will exercise the funds to expand its customer basis and to extend its portfolio of PLLs, DLLs and LDOs for exercise in semiconductor and system designs at advanced process nodes.
Imec and Synopsys completed a comprehensive sub-3nm parasitic variation modeling and retard sensitivity study of complementary FET (CFET) architectures. The QuickCap NX 3D territory solver was used by Synopsys R&D and imec research teams to model the parasitics for a variety of device architectures and to identify the most captious device dimensions and properties, which allowed for optimization of CFET devices for better power/performance trade-offs.
Credo utilized Moortec’s Temperature Sensor and Voltage Monitor IP to optimize performance and extend reliability in its latest generation of SerDes chips. Moortec’s PVT sensors are utilized in every bit of Credo measure products which are being deployed on system OEM linecards and 100G per lambda optical modules. Credo cited ease of integration and reduced time-to-market and project risk.
Wave Computing selected Mentor’s Veloce Strato emulation platform for functional verification and validation of its latest Dataflow Processor Unit chip designs, which will live used in the company’s next-generation AI system. Wave cited capacity and scaling advantages, breadth of virtual exercise models, reliability, and determinism as behind the choice.
MaxLinear adopted Cadence’s Quantus and Tempus timing signoff tools in developing the MxL935xx Telluride device, a 400Gbps PAM4 SoC using 16FF process technology. MaxLinear estimated they got 2X faster multi-corner extraction runtimes versus single-corner runs and 3X faster timing signoff flow.
The European Processor Initiative selected Menta as its provider of eFPGA IP. The EPI, a collaboration of 23 partners including Atos, BMW, CEA, Infineon and ST, has the objective of co-designing, manufacturing and bringing to market a system that supports the high-performance computing requirements of exascale machines.Jesse Allen (all posts)Jesse Allen is the learning seat administrator and a senior editor at Semiconductor Engineering.
Microsoft announced on Monday that recent tools occupy been released to serve further extend the compatibility and interoperability of Office Open XML (OOXML) document formats used in Microsoft Office 2007.
The recent tools are being developed by various open source projects. In addition, the Fraunhofer Fokus research group is working on a future "test library and validation tool" that will check document formats to espy how well they comply with ISO/IEC 29500 and ECMA-376, which are OOXML-based international standards. Microsoft is a colleague in the validation appliance effort, which was announced in late February.
One of the open source projects releasing a recent appliance is Apache POI, which works to accomplish OOXML files readable in Java-based applications. On Monday, Apache POI 3.5 beta 5 was released at the Apache POI Web site, along with a software development kit. This latest release adds "improved support" for .DOCX (Word) and .PPTX (PowerPoint) file formats, as well as "extended support" for the .XLSX (Excel) file format, according to a Microsoft announcement. Microsoft first began collaborating with the Apache POI project back in March of ultimate year.
On Friday, MindTree and Microsoft released the Open XML Document Viewer v1.0 application. This browser plug-in, available at the CodePlex open source project site, allows Microsoft Office 2007 documents to live read in a Web browser. The Open XML Document Viewer, which translates OOXML-based files to HTML, now supports the Opera browser on both Windows and Linux. Other supported browsers comprehend Firefox and Internet Explorer versions 7 and 8.
Microsoft and Dialogika occupy enhanced an Office Binary to Open XML Translator application by adding support for .XLS and .PPT files. This application lets the user translate Office binary files into OOXML and OpenDocument Format (ODF) files. The aspect III final version of the translator was released on SourceForge in late April.
Finally, the Open XML-ODF Translator add-in for Microsoft Office got some improvements with version 3.0, which was released in late March on SourceForge. Microsoft supported ODF 1.1 with this translator release.
Native support for ODF 1.1 is now allotment of Microsoft Office 2007 Service Pack 2, which was released in late April. However, the quality of that support has sparked an open spat among OASIS Technical Committee members who are currently overseeing the ODF international standard.
A blog entry by Rob Weir, IBM's chief ODF architect and chair of the ODF Technical Committee at OASIS, accused Microsoft of either incompetence or sabotage by not supporting an ODF namespace convention that helps translate formulas in spreadsheets between applications. In response, Gray Knowlton, a Microsoft group product manager, called for Weir to "step down as chairman." Microsoft and IBM quiet occupy some rotten blood left over from a contentious ISO/IEC OOXML standardization process and both are now participants in the OASIS ODF standards effort.
Microsoft's Doug Mahugh, lead standards professional on the Office interoperability team, explained in his blog that the ODF measure doesn't specify the code-handing details for formulas sufficiently enough. He claimed that even IBM's Lotus Symphony spreadsheet has a problem translating formulas to other ODF-based spreadsheets, such as Sun's OpenOffice.org. In a later blog entry, Mahugh said that ODF document changes aren't being supported in Microsoft Word's ODF implementation OOXML because of technical issues and unclear ODF documentation in the ODF specification, among other details.
"Tracked changes are essential to document collaboration, and formulas are the essence of spreadsheets. Microsoft's failure to support either in SP2 is revealing with admiration to its support for real-world interoperability," stated Marino Marcich, managing director of the ODF Alliance, an industry trade group promoting ODF, in a released statement.
The upshot of these spats, according to a Burton Group blog, is that there are quiet major compatibility problems between the ODF and OOXML document formats. The blog emphasized that enterprises should stick with the document formats they currently exercise in their office productivity software until such kinks glean worked out. The blog furthermore preeminent that ODF 1.2, when it's released, will likely occupy an Open Formula syntax that will decipher the current impasse.
Kurt Mackie is senior intelligence producer for the 1105 Enterprise Computing Group.
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