Forthcoming and Online First Articles

International Journal of Big Data Intelligence

International Journal of Big Data Intelligence (IJBDI)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Big Data Intelligence (2 papers in press)

Regular Issues

  • Map reduce-based scalable Lempel-Ziv and application in route prediction   Order a copy of this article
    by Vishnu Shankar Tiwari, Sudha Chaturvedi, Arti Arya 
    Abstract: Prediction of route based on historical trip observation of users is widely employed in location-based services. This work concentrates on building a route prediction system using Lempel-Ziv technique applied to a historical corpus of user travel data. Huge continuous logs of historical GPS traces representing the user’s location in past are decomposed into smaller logical units known as trips. User trips are converted into sequences of road network edges using a process known as map matching. Lempel-Ziv is applied on road network edges to build the prediction model that captures the user’s travel pattern in the past. A two-phased model is proposed using a map reduce framework without losing accuracy and efficiency. Model is then used to predict the user’s end-to-end route given a partial route travelled by the user at any point in time. The objective of the proposed work is to build a Route Prediction system in which model building and prediction both are horizontally scalable.
    Keywords: Lempel-Ziv; route prediction; horizontally scalable; map reduce; distributed computing.
    DOI: 10.1504/IJBDI.2022.10047952
     
  • Resource monitoring framework for big raw data processing   Order a copy of this article
    by Mayank Patel, Minal Bhise 
    Abstract: Scientific experiments, simulations, and modern applications generate large amounts of data. Analysing resources required to process such big datasets is essential to identify application running costs for cloud or in-house deployments. Researchers have proposed keeping data in raw formats to avoid upfront utilisation of resources. However, it poses reparsing issues for frequently accessed data. The paper discusses detailed comparative analysis of resources required by in-situ engines and traditional database management systems to process a real-world scientific dataset. A resource monitoring framework has been developed and incorporated into the raw data query processing framework to achieve this goal. The work identified different query types best suited to a given data processing tool in terms of data to result time and resource requirements. The analysis of resource utilisation patterns has led to the development of query complexity aware (QCA) and resource utilisation aware (RUA) data partitioning techniques to process big raw data efficiently. Resource utilisation data have been analysed to estimate the data processing capacity of a given machine.
    Keywords: big raw data; database management systems; DBMSs; in-situ engines; query processing; resource monitoring.
    DOI: 10.1504/IJBDI.2023.10053408
     

 

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