Debnath Mukherjee

Ad-hoc ride sharing application using continuous SPARQL queries

In the existing ride sharing scenario, the ride taker has to cope with uncertainties since the ride giver may be delayed or may not show up due to some exigencies. A solution to this problem is discussed in this paper. The solution framework is based on gathering information from multiple streams such as traffic status on the ride givers routes and the ride givers GPS coordinates. Also, it maintains a list of alternative ride givers so as to almost guarantee a ride for the ride taker. This solution uses a SPARQL-based continuous query framework that is capable of sensing fast-changing real-time situation. It also has reasoning capabilities for handling ride takers preferences. The paper introduces the concept of user-managed windows that is shown to be required for this solution. Finally we show that the performance of the application is enhanced by designing the application with short incremental queries.

A context-aware recommendation system considering both user preferences and learned behavior

Context awareness is an important aspect to be considered for intelligent recommendation systems. In this paper we consider the TV recommendation scenario. We argue that content-based recommendation is best suited for an environment where a database of similar users ratings of the program is not available. Also, it is important to consider both user preferences as well as learned user behavior. We present our design of a context-aware TV recommender which considers the users context, users preferences and users TV viewing behavior. We consider an algorithm reported in the literature which uses user modeling to learn users viewing habits, and extend this algorithm to add context-based and user preference-based recommendations. Finally, we present our results of a user study which validates the efficiency of our algorithm.

An architecture for real time television audience measurement

Currently, audience measurement reports of television programs are only available after a significant period of time, for example as a daily report. This paper proposes an architecture for real time measurement of television audience. Real time measurement can give channel owners and advertisers important information that can positively impact their business. We show that television viewership can be captured by set top box devices which detect the channel logo and transmit the viewership data to a server over internet. The server processes the viewership data and displays it in real time on a web-based dashboard. In addition, it has facility to display charts of hourly and location-wise viewership trends and online TRP (Television Rating Points) reports. The server infrastructure consists of in-memory database, reporting and charting libraries and J2EE based application server.

Windowing mechanisms for web scale stream reasoning

Web-scale stream reasoning is based on continuous queries and reasoning on a snapshot of the dynamic knowledge combined with background knowledge. The existing stream reasoners usually use either time-based or count-based window techniques following the data stream principles, however they do not fit all scenarios in the stream reasoning area. In this paper, different types of windowing mechanisms are described with exemplary scenarios in which they are most suitable for reasoning on stream of facts. A new windowing technique namely Adaptive Window is also proposed. Lastly, some important questions related to windowing techniques for web-scale stream reasoning are positioned.

Towards Efficient Stream Reasoning

We present a stream reasoning system, QUARKS, which has features like knowledge packets, application managed window and incremental query. Combination of rules and continuous queries along with application optimization has been used to address high performance requirements. Experimental results show that our proposed methodology is effective.

Capital market surveillance using stream processing

Exchanges and regulators need effective tools for surveillance and monitoring for timely detection and prevention of fraudulent activities such as market manipulation, price rigging and insider trading. In this paper we describe how Complex Event Processing (CEP) technology can be used in real time detection of potential fraudulent activity. We introduce the general concepts of CEP and how it can be used in Banking and Financial Markets for real time fraud detection. Finally we demonstrate how event stream processing using a data stream management system (DSMS) is superior to a traditional solution designed using a DBMS. We present results obtained after from using a commercial event stream processing system (IBMs InfoSphere Streams platform) for certain typical low latency fraud detection scenarios.

Towards a Universal Notification System

As the world is getting more and more connected, a requirement for a connected notification system has emerged. In this paper a universal notification system namely UNS based on stream reasoning is described that not only meets the requirement of knowledge sharing among applications but can cater to different varying and custom scenarios, is flexible and semantic web compliant. Experimentation was done considering a meeting use case in a simulated condition on real data of smart city, and the experimental results were found to be promising.

Energy Monitoring IoT Application using Stream Reasoning.

We consider the application of stream reasoning to the problem of monitoring energy consumption of a premises with buildings, each building having multiple floors. The floors have energy meters in several categories such as AC, UPS and Lighting. The objective is to compute the real-time aggregate energy consumption and alert whenever energy consumption thresholds are crossed, at the building, floor or metertype level, thus determining whether there is overloading. We also want to have a solution that can be easily applied to a large number of floors and buildings. We show how just a few continuous SPARQL queries and performance enhancing rules can implement the solution. Finally we compare the performance of queries with and without the HAVING clause and with and without using entailments from rules.

Workflow for the Internet of Things.

Business Processes are an important part of a business. Businesses need to meet the SLA (Service Level Agreements) required by the customers. KPI (Key Performance Indicators) measure the efficiency and effectiveness of the business processes. Meeting SLA and improving the KPIs is the goal of an organization. In this paper, we describe the benefits of workflow technology for the IoT (Internet of Things) world. We discuss how workflows enable tracking of the state of various processes, thus giving the business owner an insight into the state of the business. We discuss how by defining IoT workflows, prediction of imminent violation of SLA can be achieved. We describe how IoT workflows can be triggered by the low level IoT messages. Finally, we show the architecture of an IoT workflow management system and present experimental results.

Incremental time series algorithms for IoT analytics: an example from autoregression

The Internet of Things (IoT) is emerging as an important application area for time series statistical analysis and data mining of time series. As the volume of sensor data is high, time series analysis of sensor data is a problem of processing large datasets. Moreover, the IoT platforms have to simultaneously process multiple jobs on the same infrastructure. Processing such large datasets requires large amount of memory. To alleviate this problem, we propose use of incremental algorithms. Incremental algorithms can be used for both batch and streaming applications. In this paper, we show an incremental algorithm for an example time series analysis algorithm viz. autoregression. We describe a memory efficient autoregression algorithm and show the memory footprint reduction achieved by using this incremental algorithm.