BITS Faculty Publications

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    Dimensions influencing business intelligence and analytics maturity: a critical analysis
    (Inder Science, 2021-05) Tikoria, Jyoti
    The importance of business intelligence and analytics (BI&A) as a capability in an organisation has grown over the years. In order to assess the state of BI&A, managers today need to identify the critical dimensions which influence the maturity of BI&A capability. A wide range of maturity models (MMs) have been developed, for the purpose of assessment of BI&A capability maturity. Managers may find it difficult to select the appropriate MM to use especially with the changing characteristics of data over time and advent of big data. Each of these models has multiple dimensions which are not necessarily exhaustive and distinct. This study consolidates the large number of dimensions found across 29 BI&A MMs in extant literature. With help of an expert panel, a set of six distinct and critical dimensions needed for assessing BI&A capability in organisations have been identified. Qualitative research tool NVIVO is used for analysis of the research articles.
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    Pathway and Future of IoE in Smart Cities: Challenges of Big Data and Energy Sustainability
    (CRC, 2021) Tripathi, Sharda
    This chapter deals with the IoT node-level and network energy sustainability and IoE stability aspects, wherein the benefits from node-level and network intelligence are presented. Some field IoT sensing applications, such as in smart cities, pollution monitoring, and agricultural automation, are characterized by high energy consumption, node lifetime criticality, and energy sustainability issues. More discussion on smart meter data compressibility and novel approaches to dealing with big data concerns caused by millions of smart meter installation as a part of smart city monitoring exercise are presented in the chapter. It has been demonstrated how the challenges of big data and energy sustainability are addressed via application-specific unique approaches. The research state-of-the-art and open issues on mitigating big data challenges and energy sustainability have been highlighted. Further, the role of smart grid on uninterrupted IoE operation, the benefits of distributed energy generation through IoE, and the newer challenges of power grid stability/controllability have also been discussed.
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    Fuzzy Query Processing in Distributed databases
    (ICAISC, 2016) Bhanot, Surekha
    The problem of evolving databases to make them more intuitive, user-friendly and to be able to answer vague human queries with separate needs for each user has become a popular research topic. The solution to this problem in part has been proposed via databases that aim at inserting fuzzy data into databases hence handling vague human like queries. It has been suggested in many research papers that fuzziness may be applied to databases. However, this approach is infeasible and inefficient for real time processing. In the past 30 years of research, fuzzy databases are still not popular in industry because of unwillingness of companies to replace crisp data with fuzzy data in their databases due to excessive precomputation and possible chances of data inconsistency. Having fuzzy databases also places severe constraints on the database as it will become very difficult to run crisp queries on fuzzy databases. This problem becomes even more complex with the advent of “Big Data”. This paper proposes a three pronged fuzzy logic based technique as a layer of computation above traditional query processing to solve such queries in real time. This fuzzy logic based approach to querying in distributed databases can be used to solve ambiguous queries, incorporating the preferences of each user in the current scenario of excessive data. The results obtained using the fuzzy logic approach are compared with those obtained using traditional approach in terms of accuracy, time taken for each approach and closeness of the results to users requirements.
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    Incremental MapReduce for K-Medoids Clustering of Big Time-Series Data
    (IEEE, 2018) Jangiti, Saikishor
    There is a high necessity to refresh the data mining results, as the former results become stale and obsolete over time due to dynamic and evolving data. Clustering is one of the important data mining techniques that help to group data points with similarity together. To mine the data generated exponentially in these days, MapReduce, a parallel programming framework can be combined MapReduce with the k-medoids clustering algorithm to arrive at the optimum results quickly. Due to the parallel processing architecture of Hadoop, the proposed iterative algorithm for processing incremental data using an intermediate key file exhibited better performance over conventional k-medoids.
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    Catur Approach to Assess the Quality of Big Data Using Decision Tree and Multidimensional Model
    (AENSI Publisher, 2015) K., Pradheep Kumar
    This paper is intended to design and develop multidimensional and decision tree based frameworks, for assessing the quality of a big data. Since the datasets represented in a big data environment is both complex and multidimensional, the quality of big data can be better viewed through multiple dimensions. Most enterprises face number of challenges in managing the quality of the big data during their initial setup or migration from traditional database or after building the big data. This paper uses multidimensional model proposed for Knowledge Management System for designing critical quality dimensions for big data. Based on the extensive literature review, this work proposes a classification of big data quality into many quality factors such as accessibility, consistency, integrity, usability, relevance, completeness, compatibility, conformity and accuracy. Since there are very few appropriate data stewards or frameworks available for confirmation of quality dimensions, this paper aims to develop some hybrid approaches using multi-dimensional model and decision tree based methods for automatic quality checks. Using decision tree, multiple if-then rules can be formed to decide on the quality of data based on the specific constraints developed for big data. The paper also aims to provide the quality framework and measures which can serve as a data quality firewall just like an internet firewall to proactively find the quality issues and apply the rules based on the decision tree algorithms to prevent bad or inconsistent or invalid data or access entering in to the big data environment.
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    Fuzzy-Based Querying Approach for Multidimensional Big Data Quality Assessment
    (2017) K., Pradheep Kumar
    This paper is intended to design a fuzzy based approach to assess standards and quality of big data. It also serves as a platform to organizations that intend to migrate their existing database environment to big data environment. Data is assessed using a multidimensional approach based on quality factors like accuracy, completeness, reliability, usability, etc. These factors are analysed by constructing decision trees to identify the quality aspects which need to be improved. In this work fuzzy queries have been designed. The queries are grouped as sets namely Excellent, Optimal, Fair and Hybrid. Based on the fuzzy data sets formed and the query compatibility index, a query set is chosen. A data set that has a very high degree of membership is assigned a fair query set. A data set with a medium degree of membership is assigned a optimal query set. A data set that has a lesser degree of membership is assigned a Excellent query set. A data set which needs a combination of queries of all the above is assigned a hybrid query set. The fuzzy query based approach reduces the query compatibility index by 36%, compared to a normal query set approach.
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    Unpredictable Password Generation using Graphical Authentication and Decentralized Encryption
    (IJSEAS, 2016) Ganesan, Akshaya
    Abstract—Privacy is which data can be safely disclosed without leaking sensitive information. The objective of a knowledge based secure system is to select stronger passwords for the users and to provide them secret keys. In this paper, a multi-authority decentralized encryption scheme is proposed which provides secret keys without knowing the global identifier of the user. This scheme issues secret keys without any cooperation from the different authorities. Any authority is free to join or leave the system. Users can select passwords of higher strength using click-points. Persuasive technology is used for generating graphical passwords. Keywords-Graphical passwords,privacy,decentralized encryption,secure system
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    CTI-Twitter: Gathering Cyber Threat Intelligence from Twitter using Integrated Supervised and Unsupervised Learning
    (IEEE, 2020) Agarwal, Vinti
    Cyber threat intelligence (CTI) can be gathered from multiple sources, and Twitter is one such open source platform where a large volume and variety of threat data is shared every day. The automated and timely mining of relevant threat knowledge from this data can be crucial for enrichment of existing threat intelligence platforms to proactively defend against cyber attacks. We propose CTI-Twitter: a novel frame-work combining supervised and unsupervised learning models to collect, process, analyze and generate threat specific knowledge from tweets coming from multiple users. CTI-Twitter has multi-fold contributions: i) first collecting tweets through Twitter API, ii) extracting relevant threat tweets from irrelevant ones, and classifying relevant ones into multiple classes of threats iii) then grouping tweets belonging to each class using topic modeling iv) finally performing data enrichment and verification process. We evaluate our proposed model on real-time tweets collected for about four months (in year 2020) using Twitter API. The encouraging results obtained indicate the effectiveness of CTI-Twitter in terms of timeliness and discovery of trending attacks patterns, and vulnerabilities.
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    Unwanted Traffic Identification in Large-Scale University Networks: A Case Study
    (Springer, 2016) Narang, Pratik
    To mitigate the malicious impact of P2P traffic on University networks, in this article the authors have proposed the design of payload-oblivious privacy-preserving P2P traffic detectors. The proposed detectors do not rely on payload signatures, and hence, are resilient to P2P client and protocol changes—a phenomenon which is now becoming increasingly frequent with newer, more popular P2P clients/protocols. The article also discusses newer designs to accurately distinguish P2P botnets from benign P2P applications. The datasets gathered from the testbed and other sources range from Gigabytes to Terabytes containing both unstructured and structured data assimilated through running of various applications within the University network. The approaches proposed in this article describe novel ways to handle large amounts of data that is collected at unprecedented scale in authors’ University network.
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    Big Data Security Challenges and Preventive Solutions
    (Springer, 2019-10) Rohil, Mukesh Kumar
    Big data has opened the possibility of making great advancements in many scientific disciplines and has become a very interesting topic in academic world and in industry. It has also given contributions to innovation, improvements in productivity and competitiveness. However, at present, there are various security risks involved in the process of collection, storage and use. The leakage of privacy caused by big data poses serious problems for the users; also the incorrect or false big data may lead to wrong or invalid analysis of results. The presented work analyzes the technical challenges of implementing big data security and privacy protection, and describes some key solutions to address the issues related with big data security and privacy.