It’s hard to imagine a function that requires access to a broader and more diverse set of information than investment research. After all, the typical research analyst pours through an endless amount of data as they strive to produce timely, accurate, and relevant investment recommendations. Annual reports, company filings, security analytics, risk analytics, industry blogs, social media, and market news are just a few of the many data points leveraged as part of the research process.
One of the biggest changes in the upcoming Sherlock release of the Attivio Platform is moving to a Hadoop-based architecture for big data applications. Hadoop provides us with a number of low level capabilities that we no longer have to manage at this scale, such as resource management (YARN), coordination (Zookeeper), storage (HDFS) and bookkeeping (HBase). One of the side benefits of YARN based resource allocation is the ability to scale up or down your system’s footprint with a few simple YARN commands. At Attivio, we’ve taken this ability and used it to scale up/down our index, for total flexibility when it comes to performance, cost or index management.
Remember the frustrations of key word search? If you need a refresher, just check out If Google Was a Guy. It’s funny—in a way that makes you kind of squirm in your seat. Of course, in the early days of search engines, power searchers used Boolean query operators. We have George Boole, 18th-century English mathematician, to thank for that.
In fact, there are still Boolean purists out there who take Google, Bing and other modern search engines to task for no longer fully supporting Boolean queries. Nevertheless, natural language processing (NLP) evolved pretty much to relieve search users from the burden of having to memorize Boolean query operators and how to use them.
Some days, it seems that new machine learningapplications are popping up everywhere you look in the news. In this author’s opinion, the search market seems to have anointed machine learning as the new hotness. This is a fascinating realization, because anyone who has spent more than a couple years deploying search in the enterprise knows that machine learning has been used and applied in exciting and unique ways for years and years.
In my experience, clients tend to conflate what machine learning means when it comes to Enterprise Search. Of course, it’s not their fault – machine learning is everywhere you look! But when Attivio says machine learning, we mean two things:
Sir Arthur Conan Doyle’s 1886 fictional “consulting detective,” Sherlock Holmes, was a great mind renowned for his highly advanced powers of observation and reasoning. He was often assisted by Dr Watson, who was unfailingly loyal, if noticeably less bright. At the end of each thrilling tale starring the duo, the anxious reader would always be delighted to hear Sherlock announce that he had solved the latest mind-bending riddle, inevitably characterizing the solution to his trusty helper as, “Elementary, my dear Watson!”
Attivio, a cognitive search platform provider, is pleased to have been recognized recently by Gartner as a “Visionary” in their inaugural report on “Insight Engines.” The report offers this definition for what an Insight Engine actually does:
"Insight engines apply relevancy methods to describe, discover, organize and analyze data. This allows existing or synthesized information to be delivered proactively or interactively, and in the context of digital workers, customers or constituents at timely business moments."
One of the foundational technology differentiators of the Attivio Platform is the ability to perform Query Time Joins of data across both structured and unstructured data. Last year we received our latest patent on an extension of that technology called a “Composite Join” and it has enabled us to deliver some awesome solutions for our customers.
The Query Time Join
Before we get into composite join, let’s take a step back. The concept of a join between two tables is well understood in the realm of databases. For example:
Policies are rarely something that get people excited, but when it comes to the enterprise, they are the foundation for every risk and compliance solution. More importantly, regulators around the world and in every industry, rely on, and in many cases, require corporations to maintain and enforce policies. Policies are what keep your data private, ensure a fair playing field, and generally keep the world a safe place.
Attivio has been at the forefront of secure search-based applications for the last 8 years. Using our patented query-time join capabilities we are able to store security information in the index separate from the content and use it to apply security filters automatically to ensure that end users only see the most relevant content they are allowed to see. This allows us to automatically preserve security permissions from sources such as SharePoint, Jive, Confluence and other content repositories.
USA Today’s Marco della Cava ran an article earlier this week, “IBM, Salesforce join AI forces with Watson, Einstein.” The artificial intelligence-based partnership aims to, “boost the range of predictive analytics it can provide clients.” Salesforce’s AI Platform, Salesforce Einstein, which mines data to help salespeople close leads, is being combined with IBM Watson to provide data-based insights for businesses. The integrated solution is reported to be operational in the second half of the year.