Magazine article AI Magazine

Semantics-Empowered Big Data Processing with Applications

Magazine article AI Magazine

Semantics-Empowered Big Data Processing with Applications

Article excerpt

We discuss the nature of big data and address the role of semantics in analyzing and processing big data that arises in the context of physical-cyber-social systems. To handle volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision making. To handle variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle velocity, we seek to use continuous semantics capability to dynamically create event- or situation-specific models and recognize relevant new concepts, entities and facts. To handle veracity, we explore trust models and approaches to glean trustworthiness. These four of the five v's of big data are harnessed by the semantics-empowered analytics to derive value to support applications transcending the physical-cyber-social continuum.


Physical-cyber-social systems (PCSS) (Sheth, Anantharam, and Henson 2013) are a revolution in sensing, computing, and communication that brings together a variety of resources. The resources can range from networked embedded computers and mobile devices to multimodal data sources such as sensors and social media. The applications can span multiple domains such as medical, geographical, environmental, traffic, behavioral, disaster response, and system health monitoring. The modeling and computing challenges arising in PCSS can be organized around the five v's of big data (volume, variety, velocity, veracity, and value), which align well with our research efforts that exploit semantics, network, and statistics-empowered web 3.0.

Characteristics of the Big Data Problem

We discuss the primary characteristics of the big data problem as it pertains to the five v's. (The first three were originally introduced by Doug Laney of Gartner.)


The sheer number of sensors and the amount of data reported by sensors is enormous and growing rapidly. For example, more than 2 billion sensors have been deployed and about 250 terabytes of sensor data are generated for a New York to Los Angeles flight on a Boeing 737. (1) The Parkinson's disease data set (2) that tracked 16 people (9 patients + 7 controls) with mobile phones containing 7 sensors over 8 weeks is 12 gigabytes in size. However, availability of fine-grained raw data is not sufficient unless we can analyze, summarize, or abstract them in actionable ways. For example, from a pilot's perspective, the sensors' data processing should yield insights about whether the jet engine and the flight control surfaces are behaving normally or whether there is cause for concern. Similarly, we should be able to measure the symptoms of Parkinson's disease using sensors on a smartphone, monitor the disease's progression, and synthesize actionable suggestions to improve the quality of life of the patient. Cloud computing infrastructure can be deployed for raw processing of massive social and sensor data. However, we still need to investigate how to effectively translate large amounts of machine-sensed data into a few human-comprehensible nuggets of information necessary for decision making. Furthermore, privacy and locality considerations require moving computations closer to the data source, leading to powerful applications on resource-constrained devices. In the latter situation, even though the amount of data is not large by normal standards, the resource constraints negate the use of conventional data formats and algorithms, and instead necessitate the development of novel encoding, indexing, and reasoning techniques (Henson, Thirunarayan, and Sheth 2012).

The volume of data challenges our ability to process them. First, it is difficult to abstract fine-grained machine-accessible data into a coarse-grained human-comprehensible form that summarizes the situation and is actionable. Second, it is difficult to scale computations to take advantage of distributed processing infrastructure and, where appropriate, exploit reasoning on mobile devices. …

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