The Federal Bureau of Investigation recently released its annual report, “Crime in the United States,” which stated that in 2014 there were 1.2 million violent crimes committed in America, 63.6% of which were aggravated assaults. In addition, 8.2 million property crimes were reported by law enforcement agencies; victims suffered financial losses of approximately $14.3 billion.
However, not all larceny occurs in “the real world,” per say. In a separate FBI report on Internet crime, cybercrimes accounted for nearly 270,000 documented incidents last year. These illicit activities, which include auto and real estate fraud, government impersonation scams and extortion, resulted in total losses of $800 million.
With the sheer volume of crimes being committed on a daily basis and the severity of the resulting financial damages, clearly more could be done to deter future incidents. Unfortunately, as technology advances, criminals become more sophisticated in their methods, and it becomes even more paramount to remain multiple steps ahead.
Information fusion has its foundation in data fusion as used by military and intelligence agencies, generally defined as the use of techniques that combine data from multiples sources and gather that information in order to achieve inferences. This process would be more efficient that if the fusion was achieved by means of a single source.
Depending on the model used, there are several levels of assessment or refinement. As the fusion process goes through these different levels, the information is refined as more value is added. Information fusion can be defined as the process of merging information from disparate sources despite differences in conceptual, contextual and typographical representations, typically combining data from structured, unstructured and semi-structured resources.
The world is full of real world objects (people, places, things) and relationships (knows, likes). Information fusion works with these real world objects and relationships, and in the fusion process discovers new objects and relationships. The best way to represent these is in an object model representation.
As a writer and marketer, I’m no stranger to using catchy buzzwords to succinctly explain an important concept that many people are facing. Despite the fact that buzzwords are used so ubiquitously that they are often added to the Oxford English Dictionary (including emoji, twerk, and cakepop, to name a few), they are not as beloved in the enterprise technology industry as they are among consumers.
The irony behind our love/hate relationship with buzzwords, such as Big Data and IoT, is that everyone throws these labels around, but no one can agree on what they mean.
To dispel some of this confusion, I’m here to discuss a term that Objectivity has been at the forefront for years: Information Fusion.
Objectivity has been a pioneer in the world of Big Data, enabling leading businesses and government organizations to rapidly store, analyze, and find all the relationships and connections within their data from multiple sources, even at enormous scale.
Since our company’s founding more than 20 years ago, our solutions have continually expanded to address the most complex and demanding Big Data challenges—and today, we’re thrilled to announce the newest addition to our product line: ThingSpan, a purpose-built information Fusion platform that simplifies and accelerates an organization’s ability to deploy Industrial Internet of Things (IoT) applications.
Almost any popular, fast-growing market experiences at least a bit of confusion around terminology. Multiple firms are frantically competing to insert their own “marketectures,” branding, and colloquialisms into the conversation with the hope their verbiage will come out on top.
Add in the inherent complexity at the intersection of Business Intelligence and Big Data, and it’s easy to understand how difficult it is to discern one competitive claim from another. Everyone and their strategic partner is focused on “leveraging data to glean actionable insights that will improve your business.” Unfortunately, the process involved in achieving this goal is complex, multi-layered, and very different from application to application depending on the type of data involved.