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BigInsights and Text Analytics. As enterprises seek to gain operational efficiencies and competitive advantage through greater use of analytics, much of the new information they need to analyze is found in text documents and, increasingly, in a wide variety of social media sites and portals. A critical step in gaining insights from this information is extracting core data from huge volumes of text. That data is then available for downstream analytic, mining and machine learning tools. AQL (Annotator Query Language) is a powerful declarative, rule-based language for the extraction of information from text documents.

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Distilling Insights from Social Media Using Big Data Technologies Have you ever wondered what your customers are saying about you in Social media, and the impact it might be having on your business? This session will focus on how BigInsights and Big Data technologies can be used to glean useful and actionable insights from social media data. You'll see how data can be ingested and prepped and do text analytics on social data in real time. Using Hadoop, we'll show you how you can store and analyze your large volume of historical social media data and reference data. This talk and demo will provide an introduction to text analytics and how it is used within the IBM Big Data platform for a social media solution.

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The value of the fast growing class of big data technologies is the ability to handle high velocity and volumes of data. However, a lack of robust security and auditing capabilities are holding organizations back from fully using the potential of these systems. Learn how you can use Big Data technologies to help you meet this compliance and data protection challenge head on so you can return to innovating for competitive advantage. Using InfoSphere Guardium and BigInsights, we'll show you how you can meet your Hadoop security, compliance and audit requirements.

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Gain New Insights by Analyzing Machine Logs using Machine Data Analytics and BigInsights. Half of Fortune 500 companies experience more than 80 hours of system down time annually. Spread evenly over a year, that amounts to approximately 13 minutes every day. As a consumer, the thought of online bank operations being inaccessible so frequently is disturbing. As a business owner, when systems go down, all processes come to a stop. Work in progress is destroyed and failure to meet SLA’s and contractual obligations can result in expensive fees, adverse publicity, and loss of current and potential future customers. Ultimately the inability to provide a reliable and stable system results in loss of $$$’s. While the failure of these systems is inevitable, the ability to timely predict failures and intercept them before they occur is now a requirement. A possible solution to the problem can be found is in the huge volumes of diagnostic big data generated at hardware, firmware, middleware, application, storage and management layers indicating failures or errors. Machine analysis and understanding of this data is becoming an important part of debugging, performance analysis, root cause analysis and business analysis. In addition to preventing outages, machine data analysis can also provide insights for fraud detection, customer retention and other important use cases.

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