Om Prasad Patri

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The paper presents a comprehensive theoretical framework for modeling events and semantics of event processing at a level of abstraction that captures the different processes in industrial applications but is not limited to a specific application domain. The model, called Process-oriented Event Model (PoEM), provides a formal approach to model real-world(More)
We study the problem of identifying discriminative features in Big Data arising from heterogeneous sensors. We highlight the heterogeneity in sensor data from engineering applications and the challenges involved in automatically extracting only the most interesting features from large datasets. We formulate this problem as that of classification of(More)
We describe a novel method for electricity load disaggregation based on the machine learning method of time series shapelets. We frame the electricity disaggregation problem as that of event detection and event classification from time series data. We use existing shapelet-based algorithms to separate appliance activity periods (caused by switching on/off(More)
Several operations in the Exploration and Production (E&P) sector are event-driven in nature and are supported by specialized systems and applications. Narrow focus of applications results in application silos that restrict the information sharing across verticals, which is a critical requirement for coordinated cross-functional efforts. Effective response(More)
Enterprise Integration Patterns are a set of design patterns for linking multiple systems using asynchronous messaging interfaces. This approach to system integration is increasingly popular due to its relatively simple loose coupling requirement. Implementations of these patterns are available in current integration frameworks but these are not semantic in(More)
Increasing instrumentation of the modern digital oilfield produces streams of data from sensors that monitor the functioning of different components in the field. This data should be converted to actionable information rapidly in order to respond to events as they happen or are predicted. The challenge is therefore to develop technologies that can process(More)
This correspondence presents an open-source tool AutoAmp developed at the Indian Institute of Technology, Guwahati. It is available at This tool helps the user to design different types of electronic amplifiers, using solid state devices, for a given specification. It can handle several types of designs namely(More)
The recent rise in scale of sensors has led to the need for faster processing of events from multiple sensor data streams in a variety of real-world applications. We need an approach to model real-world entities and their interrelationships, and specify the process of moving from sensor data streams to event detection to event-based goal planning. Recent(More)
We address the problem of record linkage and semantic integration in the context of large collections of user-generated content. These datasets are often large since it contains the contributions of millions of Internet users. We present an approach based on approximate string matching between the metadata associated with such data. The discovered linkages(More)
The increasingly large number of sensors and instruments in the oil and gas industry, along with novel means of communication in the enterprise has led to a corresponding increase in the volume of data that is recorded in various information repositories. The variety of information sources is also expanding: from traditional relational databases to time(More)