Prateep Misra

Smart city surveillance: Leveraging benefits of cloud data stores

The smart cities of future need to have a robust and scalable video surveillance infrastructure. In addition it may also make use of citizen contributed video feeds, images and sound clips for surveillance purposes. Multimedia data from various sources need to be stored in large scalable data stores for compulsory retention period, on-line, off-line analytics and archival. Multimedia feeds related to surveillance are voluminous and varied in nature. Apart from large multimedia files, events detected using video analytics and associated metadata needs to be stored. The underlying data storage infrastructure therefore needs to be designed for mainly continuous streaming writes from video cameras and some variety in terms of I/O sizes, read-write mix, random vs. sequential access. As of now, the video surveillance storage domain is mostly dominated by iSCSI based storage systems. Cloud based storage is also provided by some vendors. Taking in account the need for scalability, reliability and data center cost minimization, it is worth investigating if large scale video surveillance backend can be integrated to the open source cloud based data stores available in the “big data” trend. We developed a multimedia surveillance backend system architecture based on the Sensor Web Enablement framework and cloud based “key-value” stores. Our framework gets data from camera/ edge device simulators, splits media files and metadata and stores those in a segregated way in cloud based data stores hosted on Amazons EC2. We have benchmarked performances of a few cloud based key-value stores under large scale video surveillance workload and demonstrated that those perform satisfactorily, bringing in inherent scalability and reliability of a cloud based storage system to a video surveillance system for a smart safe city. With a case study of the storage of video surveillance system, we show in this paper that with the availability of several cloud based distributed data stores and benchmarking tools, an applications data management needs can be served using hybrid cloud based data stores and selection of such stores can be facilitated using benchmark tools if the application workload characteristics are known.

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.

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.

An Erasure Coded Archival Storage System

There is an ever increasing need of storage capacity for storage of digital archives and historical data-digital preservation, because of regulatory and compliance requirements. There is an increasing interest in disk based archival system. Major technical challenges in creating large disk based storage archive are - providing large capacity at low costs, large read and write throughput, data integrity and sustaining hardware and operating system refresh. In this paper we present the architecture and working principle of an archival storage system that uses an erasure-coded redundancy scheme. We present the design of a Quality of Service (QoS) framework that tries to achieve an optimum balance between file availability, performance and system availability. The design includes a file encoding and placement scheme that allows files to be read from the archive without the need to access any metadata. Finally, we present the results obtained from running an experimental setup on Amazon Web Services.

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.

A temporal constraint based planning approach for city tour and travel plan generation

In this paper, we present an AI planning-based city tour and travel application. The system assists the travellers to generate temporally constrained plans and their interactive map-based visualizations. We discuss the requirement of temporal constraints in the domain of city tour and travel application and consequent modelling of such an application as a temporally constrained planning problem. We have proposed schemes for specification and processing of temporal constraints in a Hierarchical Task Network (HTN) planner. We have redefined the basic PDDL3.0 temporal operators by imposing interval bounds and defined them as a restricted subset of Metric Interval Temporal Logic (MITL) family. The planner has been used to devise city tour and travel plans under temporal constraints and the generated plans have been rendered through Google-map based navigation interface.