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000-N07 | IBM Optimization Technical Mastery Test v1

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000-N07 - IBM Optimization Technical Mastery Test v1 - braindump

Vendor IBM
Exam Number 000-N07
Exam Name IBM Optimization Technical Mastery Test v1
Questions 30 Q & A
Recent Update March 18, 2019
Free PDF Download 000-N07 Brain Dump
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000-N07 exam Dumps Source : IBM Optimization Technical Mastery Test v1

Test Code : 000-N07
Test Name : IBM Optimization Technical Mastery Test v1
Vendor Name : IBM
Q&A : 30 Real Questions

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IBM IBM Optimization Technical Mastery

IBM Db2 query Optimization the usage of AI | killexams.com Real Questions and Pass4sure dumps

In September 2018, IBM introduced a new product, IBM Db2 AI for z/OS. This synthetic intelligence engine displays facts access patterns from executing SQL statements, uses computer learning algorithms to select greatest patterns and passes this counsel to the Db2 question optimizer for use by way of subsequent statements.

computing device researching on the IBM z Platform

In can also of 2018, IBM announced edition 1.2 of its desktop researching for z/OS (MLz) product. here is a hybrid zServer and cloud software suite that ingests efficiency data, analyzes and builds models that characterize the fitness reputation of a variety of indications, displays them over time and provides actual-time scoring capabilities.

several facets of this product providing are geared toward aiding a community of model builders and executives. for instance:

  • It supports distinct programming languages akin to Python, Scala and R. This allows for facts modelers and scientists to use a language with which they're typical;
  • A graphical user interface referred to as the visual mannequin Builder courses mannequin developers without requiring highly-technical programming capabilities;
  • It comprises varied dashboards for monitoring model results and scoring functions, as well as controlling the device configuration.
  • This desktop researching suite become at the beginning geared toward zServer-based mostly analytics functions. one of the most first glaring choices turned into zSystem performance monitoring and tuning. equipment management Facility (SMF) information that are automatically generated by the working system deliver the raw statistics for system aid consumption equivalent to relevant processor usage, I/O processing, reminiscence paging and the like. IBM MLz can bring together and store these statistics over time, and build and educate models of equipment behavior, score these behaviors, determine patterns now not conveniently foreseen by humans, improve key efficiency warning signs (KPIs) after which feed the model results again into the system to affect device configuration adjustments that may enrich efficiency.

    The subsequent step was to put in force this suite to analyze Db2 efficiency facts. One solution, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the laptop studying technology to Db2 operational information to profit an figuring out of Db2 subsystem health. it could actually dynamically build baselines for key efficiency indicators, deliver a dashboard of these KPIs and provides operational staff precise-time perception into Db2 operations.

    while widespread Db2 subsystem efficiency is an important aspect in standard application fitness and performance, IBM estimates that the DBA guide group of workers spends 25% or more of its time, " ... fighting entry direction complications which cause performance degradation and repair influence.". (See Reference 1).

    AI involves Db2

    agree with the plight of modern DBAs in a Db2 ambiance. In latest IT world they should assist one or greater massive information applications, cloud utility and database functions, utility installation and configuration, Db2 subsystem and software efficiency tuning, database definition and administration, catastrophe recovery planning, and more. question tuning has been in existence considering that the origins of the database, and DBAs are continually tasked with this as neatly.

    The coronary heart of question direction evaluation in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to entry the data, experiences the areas of the objects to be accessed and develops a list of candidate records entry paths. These entry paths can encompass indexes, desk scans, numerous desk join methods and others. within the facts warehouse and large information environments there are usually additional decisions available. One of these is the existence of abstract tables (on occasion referred to as materialized question tables) that comprise pre-summarized or aggregated facts, for that reason enabling Db2 to stay away from re-aggregation processing. an extra choice is the starjoin access path, usual within the information warehouse, the place the order of desk joins is changed for performance motives.

    The Optimizer then stories the candidate entry paths and chooses the access route, "with the lowest charge." cost during this context means a weighted summation of aid utilization including CPU, I/O, reminiscence and different materials. at last, the Optimizer takes the lowest can charge entry path, retailers it in reminiscence (and, optionally, in the Db2 directory) and starts off access route execution.

    large statistics and information warehouse operations now encompass utility suites that permit the business analyst to make use of a graphical interface to construct and manipulate a miniature information model of the information they are looking to analyze. The applications then generate SQL statements based on the clients’ requests.

    The difficulty for the DBA

    with a purpose to do first rate analytics on your dissimilar facts shops you need a good knowing of the information necessities, an knowing of the analytical capabilities and algorithms available and a high-efficiency facts infrastructure. alas, the number and site of statistics sources is increasing (each in dimension and in geography), facts sizes are growing, and functions continue to proliferate in number and complexity. How may still IT managers support this environment, notably with probably the most experienced and mature body of workers nearing retirement?

    consider additionally that a huge part of reducing the full cost of ownership of these methods is to get Db2 applications to run faster and more correctly. This continually interprets into using fewer CPU cycles, doing fewer I/Os and transporting much less information throughout the community. because it is often complex to even identify which applications might advantage from efficiency tuning, one approach is to automate the detection and correction of tuning concerns. this is the place computing device learning and synthetic intelligence can also be used to exquisite impact.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 edition 12 on z/OS makes use of the computer researching amenities outlined above to accumulate and shop SQL query textual content and access path details, in addition to actual efficiency-linked historical suggestions reminiscent of CPU time used, elapsed instances and outcomes set sizes. This providing, described as Db2 AI for z/OS, analyzes and outlets the facts in computing device getting to know fashions, with the model evaluation results then being scored and made obtainable to the Db2 Optimizer. The subsequent time a scored SQL commentary is encountered, the Optimizer can then use the model scoring data as enter to its access direction alternative algorithm.

    The result should be a reduction in CPU consumption as the Optimizer uses model scoring input to choose more advantageous access paths. This then lowers CPU charges and speeds utility response instances. a major skills is that the usage of AI software does not require the DBA to have data science potential or deep insights into query tuning methodologies. The Optimizer now chooses the most fulfilling entry paths primarily based not most effective on SQL question syntax and records distribution records however on modelled and scored historic efficiency.

    This can be principally vital in case you shop facts in varied locations. as an instance, many analytical queries in opposition t big records require concurrent access to certain information warehouse tables. These tables are often referred to as dimension tables, and that they contain the facts facets constantly used to manage subsetting and aggregation. as an example, in a retail atmosphere believe a table known as StoreLocation that enumerates each shop and its location code. Queries towards save earnings information may also wish to combination or summarize income by means of region; therefore, the StoreLocation desk will be used by means of some big records queries. in this environment it is average to take the dimension tables and duplicate them regularly to the large facts software. in the IBM world this place is the IBM Db2 Analytics Accelerator (IDAA).

    Now think about SQL queries from both operational functions, data warehouse clients and massive information enterprise analysts. From Db2's viewpoint, all these queries are equal, and are forwarded to the Optimizer. youngsters, within the case of operational queries and warehouse queries they should surely be directed to entry the StoreLocation table within the warehouse. on the other hand, the query from the business analyst towards big data tables should likely access the copy of the desk there. This results in a proliferations of abilities access paths, and more work for the Optimizer. luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to make sensible access path choices.

    the way it Works

    The sequence of events in Db2 AI for z/OS (See Reference 2) is frequently right here:

  • during a bind, rebind, prepare or clarify operation, an SQL observation is handed to the Optimizer;
  • The Optimizer chooses the records access path; as the option is made, Db2 AI captures the SQL syntax, access direction alternative and query performance facts (CPU used, and so on.) and passes it to a "discovering assignment";
  • The gaining knowledge of project, which can also be achieved on a zIIP processor (a non-general-purpose CPU core that doesn't factor into application licensing fees), interfaces with the computer getting to know utility (MLz mannequin features) to save this suggestions in a mannequin;
  • because the quantity of data in every mannequin grows, the MLz Scoring service (which can also be performed on a zIIP processor) analyzes the model facts and scores the habits;
  • all over the next bind, rebind, prepare or explain, the Optimizer now has entry to the scoring for SQL models, and makes acceptable alterations to access path decisions.
  • There are additionally various user interfaces that provide the administrator visibility to the popularity of the collected SQL remark efficiency data and mannequin scoring.

    summary

    IBM's machine learning for zOS (MLz) offering is getting used to extremely good effect in Db2 version 12 to improve the performance of analytical queries as well as operational queries and their associated functions. This requires management consideration, as you need to examine that your enterprise is prepared to consume these ML and AI conclusions. How will you measure the fees and merits of the usage of computer learning? Which IT guide body of workers ought to be tasked to reviewing the influence of mannequin scoring, and maybe approving (or overriding) the consequences? How will you overview and justify the assumptions that the software makes about access path choices?

    In different phrases, how well were you aware your data, its distribution, its integrity and your latest and proposed access paths? this will check where the DBAs spend their time in helping analytics and operational utility performance.

    # # #

    Reference 1

    John Campbell, IBM Db2 exotic EngineerFrom "IBM Db2 AI for z/OS: boost IBM Db2 software efficiency with machine getting to know"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-enhance-ibm-db2-software-efficiency-with-ma

    Reference 2

    Db2 AI for z/OShttps://www.ibm.com/aid/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html


    World Banks' and companies' hobby Is shifting From Cryptocurrencies To Blockchain | killexams.com Real Questions and Pass4sure dumps

    No outcome found, are trying new key phrase!Walmart, Maersk, IBM, Barclays, JPMorgan ... to reduce the cost of the leading fiscal reporting by using 70% as a result of optimization of statistics fine, transparency and inner manage.

    IBM’s Plan to carry laptop gaining knowledge of Capabilities to records Scientists in every single place | killexams.com Real Questions and Pass4sure dumps

    Hillary Hunter is an IBM Fellow.

    Over on the IBM blog, IBM Fellow Hillary Hunter writes that the company anticipates that the area’s volume of digital records will exceed forty four zettabytes, an astonishing number. As companies begin to realize the great, untapped knowledge of statistics, they should locate a method to exploit it. Enter AI.

    IBM has worked to construct the industry’s most finished records science platform. integrated with NVIDIA GPUs and application designed specially for AI and the most statistics-intensive workloads, IBM has infused AI into offerings that customers can entry in spite of their deployment model. these days, we take the subsequent step in that event in announcing the next evolution of our collaboration with NVIDIA. We plan to leverage their new data science toolkit, RAPIDS, across our portfolio in order that our shoppers can raise the performance of laptop gaining knowledge of and facts analytics.

    Plans to promote GPU-accelerated machine studying encompass:

  • IBM POWER9 with PowerAI: to leverage RAPIDS to extend the alternatives accessible to statistics scientists with new open supply desktop discovering and analytics libraries. Accelerated workloads were confirmed to get an immediate benefit from the unique engineering that NVIDIA and IBM have achieved round POWER9, including integration of NVIDIA NVLink and NVIDIA Tesla GPUs. PowerAI is IBM’s software layer, which optimizes how nowadays’s information science and AI workloads run on these heterogeneous computing techniques. Our intention is for this improved performance trajectory for GPU-accelerated workloads on POWER9 to continue with RAPIDS.
  • IBM Watson Studio and IBM Watson desktop studying: to take abilities of the vigour of NVIDIA GPUs so that facts scientists and AI builders can construct, deploy, and run sooner fashions than CPU-handiest deployments in their AI purposes in a multicloud environment with IBM Cloud inner most for statistics and IBM Cloud.
  • IBM Cloud: to clients who opt for machines equipped with GPUs so as to practice accelerated desktop learning and analytics libraries in RAPIDS to their cloud purposes and tap into the merits of laptop researching.
  • IBM and NVIDIA’s shut collaboration through the years has helped main corporations and companies all over handle one of the world’s greatest problems,” observed Ian Buck, vp and customary supervisor of Accelerated Computing at NVIDIA. “Now, with IBM taking talents of RAPIDS open-source libraries announced today by means of NVIDIA, GPU accelerated laptop studying is coming to statistics scientists, helping them analyze huge information for insights quicker than ever feasible before. Recognizing the computing vigor that AI would need, IBM was an early recommend of records-centric programs. This method led us to carry the GPU-fitted Summit equipment, the world’s most powerful supercomputer, and already researchers are seeing colossal returns. earlier within the yr, we verified the skills for GPUs to speed up computer learning after we showed how GPU-accelerated computer getting to know on IBM power techniques AC922 servers set a new speed listing with a 46x improvement over previous consequences.

    as a result of IBM’s dedication to bringing accelerated AI to users throughout the know-how spectrum, be they users of on-premises, public cloud, deepest cloud, or hybrid cloud environments, the company is positioned to convey RAPIDS to clients in spite of how they need to entry them.

    Hillery Hunter is an IBM Fellow and CTO of Infrastructure within the IBM Hybrid Cloud business. earlier than this function, she served as Director of Accelerated Cognitive Infrastructure in IBM analysis, main a group doing go-stack (hardware through software) optimization of AI workloads, producing productiveness breakthroughs of 40x and better which have been transferred into IBM product offerings. Her technical interests have at all times been interdisciplinary, spanning from silicon know-how via gadget software, and he or she has served in technical and leadership roles in memory know-how, techniques for AI, and different areas. She is a member of the IBM Academy of expertise.

    check in for our insideHPC newsletter


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    Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, Part 2 | killexams.com real questions and Pass4sure dumps

    Ricardo Balduino and Tim Bohn

    Early Flight, Creative Commons Introduction

    As we described in Part 1 of this series, our objective is to help predict the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. We use historical flights data and historical weather data to make predictions for upcoming flights.

    Over the course of this four-part series, we use different platforms to help us with those predictions. Here in Part 2, we use the IBM SPSS Modeler and APIs from The Weather Company.

    Tools used in this use case solution

    IBM SPSS Modeler is designed to help discover patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a range of advanced algorithms and analysis techniques, including text analytics, entity analytics, decision management and optimization to deliver insights in near real-time. For this use case, we 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 also 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, we propose one possible solution for this problem. It’s not meant to be the only or the best possible 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. We encourage the readers to try and come 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 our 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 we ended up with 12 csv (comma separated value) files. We used IBM SPSS Modeler to merge all the csv files into one set and to select the ten airports in our scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in Figure 1.

    Figure 1 — gathering and preparing flights data in IBM SPSS Modeler

    Weather data — From the Extensions Hub, we 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 Figure 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, we added two new columns: ORIGIN 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 Figure 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

    · Repeat

    Figure 4 shows the first and second iterations of our process in IBM SPSS Modeler.

    Figure 4 — iterations: prepare data, run models, evaluate — and do it again First iteration

    To start preparing the data, we used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took care of null values). In order to better train the model later on, we 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 found in our input data set

    This is an interesting use case, and often a hard one to solve, due to the imbalanced data it presents, as seen in Figure 5. By “imbalanced” we mean that there were far more non-cancelled flights in the historical data than cancelled ones. We will discuss how we dealt with imbalanced data in the following iteration.

    Next, we defined which features were required as inputs to the model (such as flight date, hour, day of the week, origin and destination airport codes, and weather conditions), and which one was the target to be generated by the model (i.e. predict the cancellation status). We 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 run multiple models at once and preview their outputs, such as the area under the ROC curve, as seen in Figure 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. We decided to use the Random Trees model since the initial analysis showed it has the best area under the curve as compared to the other models in the list.

    Second iteration

    During the second iteration, we addressed the skewedness of the original data. For that purpose, we 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 our selected model work more effectively.

    Figure 7 — distribution of cancelled and non-cancelled flights after using SMOTE

    In Figure 7, we notice a more balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

    As mentioned earlier, we 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 prone to overfitting, which gives a higher likelihood of repeating the same test results when you use new data, that is, data that was not part of the original training and testing data sets. Another advantage of this method — in particular for our use case — is its ability to handle imbalanced data.

    Since in this use case we are dealing with classification analysis, we 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 Figure 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 also made wrong predictions about 4–5% of the time (false negatives and false positives).

    That was the precision given by the model using the training data set. This is also represented by the ROC curve on the left side of Figure 9. We can see, however, that the area under the curve for the training data set was better than the area under the curve for the testing data set (right side of Figure 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 false negatives and false positives).

    Figure 9 — ROC curves for the training and testing data sets

    Nevertheless, we decided that the results were still good for the purposes of our discussion in this blog, and we stopped our iterations here. We encourage readers to further refine this model or even to use other models that could solve this use case.

    Deploying the model

    Finally, we deployed the model as a REST API that developers can call from their applications. For that, we created a “deployment branch” in the SPSS stream. Then, we used the IBM Watson Machine Learning service available on IBM Bluemix here. We imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can also call 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 make the prediction.

    A typical scoring endpoint provided by the Watson Machine Learning service would look like 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 body 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 be cancelled or not. This is seen in Figure 10, which shows a call being made to the scoring endpoint — and its response — using an HTTP requester tool 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 be 88.8% likely to be cancelled, based on forecast weather conditions.

    Conclusion

    IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this use case without writing a single line of code. We were able to follow 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 also find instructions on how to download IBM SPSS Modeler, get a key for The Weather Channel APIs, and much more.


    Week In Review: Design, Low Power | killexams.com real questions and Pass4sure dumps

    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 element 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 also 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 range of interfaces to CMOS image sensors. Target applications include panel and sensor interfaces, projectors, MFP, DSC, surveillance, AR and VR, and AI.

    Analog tool and IP maker Movellus closed a second round of funding from Intel Capital. Movellus’ technology automatically generates analog IPs using digital implementation tools and standard cells. The company will use the funds to expand its customer base and to increase its portfolio of PLLs, DLLs and LDOs for use in semiconductor and system designs at advanced process nodes.

    Imec and Synopsys completed a comprehensive sub-3nm parasitic variation modeling and delay sensitivity study of complementary FET (CFET) architectures. The QuickCap NX 3D field 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 critical 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 increase reliability in its latest generation of SerDes chips. Moortec’s PVT sensors are utilized in all Credo standard 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 be used in the company’s next-generation AI system. Wave cited capacity and scaling advantages, breadth of virtual use 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 Knowledge Center administrator and a senior editor at Semiconductor Engineering.

    Office XML Document Format Tools Released | killexams.com real questions and Pass4sure dumps

    Office XML Document Format Tools Released
  • By Kurt Mackie
  • 05/19/2009
  • Microsoft announced on Monday that new tools have been released to help further extend the compatibility and interoperability of Office Open XML (OOXML) document formats used in Microsoft Office 2007.

    The new 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 see how well they comply with ISO/IEC 29500 and ECMA-376, which are OOXML-based international standards. Microsoft is a partner in the validation tool effort, which was announced in late February.

    One of the open source projects releasing a new tool is Apache POI, which works to make 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 last 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 be 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 include Firefox and Internet Explorer versions 7 and 8.

    Microsoft and Dialogika have 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 Phase 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 part 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 still have some bad 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 standard 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 regard 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 still major compatibility problems between the ODF and OOXML document formats. The blog emphasized that enterprises should stick with the document formats they currently use in their office productivity software until such kinks get worked out. The blog also noted that ODF 1.2, when it's released, will likely have an Open Formula syntax that will solve the current impasse.

    About the Author

    Kurt Mackie is senior news producer for the 1105 Enterprise Computing Group.



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