By Dr. Jans Aasman, Ph.D, CEO of Franz Inc.
Big Dataâs influence across the data landscape is well known, and virtually undeniable. Organizations are adopting a greater diversity of sources and data structures in quantities that are rapidly increasing while they want the results of analytics faster and faster.
Of great importance is also how big dataâs influence is shaping that landscape. Gartner asserted, âThe number and variety of public-facing open datasets and Web APIs published by all tiers of governments worldwide continues to increase.â The inclusion of the growing variety of public data sources shows that big data is actually also big public data.
The key is to expeditiously integrate that dataâin a well-governed, sustainable mannerâwith proprietary enterprise data for timely analytic action. Semantic graph database technology is built to facilitate data integration and as such surpasses virtually every other method for leveraging public data. The recent explosion of public sources of big data is effectively dictating the need for semantic graph databases.
The Smart Data Approach
More than any other type of analytics, public big data analysis and integration comprehensively utilizes the self-describing, smart data technologies on which semantic graph databases hinge. The exorbitant volumes and velocities of big data benefit from this intrinsic understanding of specific data elements that are expressed in semantic statements known as triples. But itâs the growing variety of data types included in integrating public and private big data sources that exploit this self-identifying penchant of semantic dataâespecially when linking disparate data sets.
This facet of smart data proves invaluable when modeling and integrating structured and unstructured (public) data during the analytic preparation process. The same methods by which proprietary data are modeled can be used to incorporate public data sources in a uniform way. When integrating unstructured or semi-structured public data with structured data for fraud detection, hedge fund analysis or other use cases, semantic graph databasesâ propensity to readily glean the meaning of data and relationship between data elements is critical to immediate responses.
Triple stores are integral to incorporating public big data with internal company sources because they provide a form of machine intelligence that is essential to expanding the understanding of how data relates to each other. Every semantic statement provides meaning about data. Triple stores utilize these statements as the basis for providing further inferences about the way that data interrelates.
For example, say the enterprise data warehouse of a hospital has data about a patient that will be expressed in triples like: patient X takes Drug Aspirin and patient X takes Drug Insulase. A publicly available medical drug database will have triples such as: Â Chlorpropamide has the brand name Insulase and ChrolPropamide has Drug Interaction with Aspirin. The reasoning in the triple stores will instantly conclude that Patient X has a problem.
Such an example illustrates the usefulness of triple stores when contextualizing public big data integrated with internal data. Firstly, this type of basic inferencing is not possible with other technologies, including both relational and graph databases that do not involve semantics. The latter are focused on the graphâs nodes and their properties; semantic graph databases focus on the relationships between nodes (the edges). Furthermore, such intelligent inferencing illustrates the fact that these stores can actually learn. Finally, such inferencing is invaluable when leveraged at scale and accounting for the numerous subtleties existent between big data, and is another way of deriving meaning from data in low latency production environments.
Public Big Data
Much of the value that public big data delivers pertains to general knowledge generated by researchers, scientists and data analysts from the government. By integrating this knowledge with big data within the enterprise we can build new applications that benefit the enterprise and society.
Dr. Jans Aasman, Ph.d is the CEO of Franz Inc., an early innovator in Artificial Intelligence and leading supplier of Semantic Graph Database technology.