Conference Papers

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    Automatic spatio-temporal indexing to integrate and analyze the data of an organization
    (Association for Computing Machinery, Inc acmhelp@acm.org, 2017) Knoblock, C.A.; Joshi, A.R.; Megotia, A.; Pham, M.; Ursaner, C.
    Organizations are awash in data. In many cases, they do not know what data exists within the organization and much information is not available when needed, or worse, information gets recreated from other sources. In this paper, we present an automatic approach to spatio-temporal indexing of the datasets within an organization. The indexing process automatically identifies the spatial and temporal fields, normalizes and cleans those fields, and then loads them into a big data store where the information can be efficiently searched, queried, and analyzed. We evaluated our approach on 600 datasets published by the City of Los Angeles and show that we can automatically process their data and can efficiently access and analyze the indexed data. © 2017 Copyright held by the owner/author(s).
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    Comparison of two data cleaning methods as applied to volatile time-series
    (Institute of Electrical and Electronics Engineers Inc., 2019) Ranjan, K.G.; Prusty, B.R.; Jena, D.
    Out-of-sample forecasting of historically observed time-series inevitably necessitates the application of a suitable data cleaning method to assist improved accuracy of the obtained results. The existing data cleaning methods though work amply with nonvolatile time series; fail when applied to a volatile time-series. In this paper, the suitability of the k-nearest neighbor approach and sliding window prediction approach is tested on a set of nonvolatile and volatile time-series. The performance comparison is carried out considering the historical record of furniture sales data, PV generation, load power, and ambient temperature data of different time-steps collected from various places in the USA. Further, the effect of parameters allied with both the methods on the preprocessing result is also analyzed. Finally, possible reforms are suggested for the appropriate preprocessing of volatile time-series. © 2019 IEEE.