Snehasis Banerjee

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.

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.

Semantic Exploration of Sensor Data

With governments and administrations releasing open linked data, and with the gradual rise of sensor deployments across the world, semantic queries on the combined sensor and linked data has become a need to provide several intelligent smart city services and applications. The data is represented in form of triples (RDF), concepts and relations in form of ontologies (OWL) and the corresponding query language is SPARQL as per standards of Semantic Web. In this paper, a system for sensor exploration is presented, which takes a set of keywords, context, data, learned and background knowledge as input and produces the intentioned result as output. The system tries to keep the underlying semantic web technologies transparent to the end user. The relevant challenges and the scope of future work is also discussed.

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.

Automation of feature engineering for IoT analytics

This paper presents an approach for automation of interpretable feature selection for Internet Of Things Analytics (IoTA) using machine learning (ML) techniques. Authors have conducted a survey over different people involved in different IoTA based application development tasks. The survey reveals that feature selection is the most time consuming and niche skill demanding part of the entire workflow. This paper shows how feature selection is successfully automated without sacrificing the decision making accuracy and thereby reducing the project completion time and cost of hiring expensive resources. Several pattern recognition principles and state of art (SoA) ML techniques are followed to design the overall approach for the proposed automation. Three data sets are considered to establish the proof-of-concept. Experimental results show that the proposed automation is able to reduce the time for feature selection to 2 days instead of 4 -- 6 months which would have been required in absence of the automation. This reduction in time is achieved without any sacrifice in the accuracy of the decision making process. Proposed method is also compared against Multi Layer Perceptron (MLP) model as most of the state of the art works on IoTA uses MLP based Deep Learning. Moreover the feature selection method is compared against SoA feature reduction technique namely Principal Component Analysis (PCA) and its variants. The results obtained show that the proposed method is effective.