by clicking on the page. A slider will appear, allowing you to adjust your zoom level. Return to the original size by clicking on the page again.
the page around when zoomed in by dragging it.
the zoom using the slider on the top right.
by clicking on the zoomed-in page.
by entering text in the search field and click on "In This Issue" or "All Issues" to search the current issue or the archive of back issues respectively.
by clicking on thumbnails to select pages, and then press the print button.
this publication and page.
displays a table of sections with thumbnails and descriptions.
displays thumbnails of every page in the issue. Click on a page to jump.
allows you to browse through every available issue.
FCW : October 15, 2012
Bi D i C Cloud computing delivers an optimized storage, computing, access and visualization environment for big data. According to NIST, cloud interoperability will potentially enable large datasets residing in di erent clouds to be interoperable with each other, increasing the ability to share, collaborate and analyze multiple very large datasets for knowledge-driven research. Cloud computing creates a unique opportunity to host, store, process and access big data in a scalable manner that enables elastic, on-demand, anytime- anywhere access from any platform, said Ashit Talukder, Information Access Division Chief in NIST s Information Technology Laboratory. According to Talukder, government institutions may soon be able to leverage cloud computing for big data challenges, to: unprecedented access to powerful tools; cycle times in time-consuming research processes; dramatically via economies of scale. Despite the potential advantages of cloud computing for helping organizations to analyze the ow of big data, Talukder maintains there are still many elements that must be improved to turn the promise of big data analytics into reality. For example, there s a need for better standards, metrics and interoperability of big data software, algorithms, hardware and infrastructure, he explained. "Advances in fundamental mathematics and statistics are needed, including machine learning for big data, analytics and pattern recognition for big data, subsampling and uncertainty metrics," he said. Talukder also cited a strong requirement for algorithmic advances in handling massive and complex data, along with better visualization and usability, better clustering, classi cation, outlier detection, security and privacy for big data. Meanwhile, technological improvements in networking, hardware and software infrastructure for big data storage, computation, and display/visualization are also needed, he explained. G meCh n er GAME CHANGING ECHNOLOG O MEE AGENC MI ION Sponsored Repor BIG DATA EMERGING USES OF BIG DATA FOR ANALYTICS Bi ic i i e o be e o e i e o e e o e io oce c e e e e i co e ie b e o e i ic e i e ce e o i c , e o e o e i io o o i . So e o e e i i ie e i be e o bi ic i c e: e o o e ic ) o e io co e io e o i e e e c o S o , Co .-b e G e I c., e co e i o e e co e - e ec i , o e e, i ic o e o e ice o e e o c be e o i i e e i i e i i i o e i ic , e e e o e c e ec i oo o e ic io e e io ee e c io e. G e e ic bi ic i i e e i o ib e o i c e e o i o i , o e o i io o i o be e co e e o ei o o i ec co o o e e e i e e i c o -b e e ice . Cloud Compu in nd Bi D Work Well To e her ource: NI CLOUD PROVIDES On dem nd self--service Ubiqui ous ne work ccess Resource poolin R pid el s ici y Hybrid (public nd priv e) Cloud wi h Res ric ed Access BIG DATA NEEDS F ul oler nce Mul iple--pro ocols Sc l bili y (s or e, memory, ne work, e c.) Sc l bili y (nodes lloc ion/ e rdown) Secure d ccess
October 30, 2012