基本情報

所属
国立研究開発法人産業技術総合研究所
学位
Phd(University of Tsukuba)
研究者番号
30742621
J-GLOBAL ID
201801004589477808
researchmap会員ID
B000330967
外部リンク

研究キーワード

  6

研究分野

  2

経歴

  3

学歴

  3

委員歴

  3

受賞

  4

論文

  33
もっとみる

MISC

  1

担当経験のある科目(授業)

  3

その他

  5
  • 2017年7月 - 2017年7月
    A unified framework for stream processing and OLAP analysis to avoid coupling multiple systems to achieve the two tasks, as coupling results in wastage of resources and increases the cost and complexity of the overall system. The proposed framework possesses all the essential operators to process data streams and can also maintain OLAP lattice nodes (materialized views) incrementally to answer OLAP queries. To demonstrate StreamingCube, a web-based GUI has been developed which can accept continuous queries and OLAP queries and display their results in the form of tables and graphs.
  • 2016年10月 - 2016年10月
    An architecture for approximate answering of OLAP queries based on Piece-wise Linear Approximation (PLA) structure. PLA is used to maintain compact summaries of raw data streams in main memory, hence can significantly reduce its maintenance cost at a small cost of querying error. For details please refer: Approximate OLAP on Sustained Data Streams (in DASFAA 2017).
  • 2016年3月 - 2016年3月
    A data stream processing framework capable of handling semi-structured JSON data streams. Its distinguished feature is smart query execution scheme capable of significantly improving the system throughput. For details please refer: Smart Query Execution for Event-driven Stream Processing (in BigMM2016).
  • 2015年9月 - 2015年9月
    MPFM is a web application developed on top of JsSpinner Stream Processing Engine (SPE). It makes use of the JsSpinner SPE to efficiently process the location stream captured through the GPS-enabled mobile devices. MPFM is equipped with a GUI to register user queries and to apply filters to the location stream. The query and filters output is displayed as a real-time people movement on Google Maps.
  • 2015年1月 - 2015年1月
    A system for detecting outliers from moving objects’ data, based on distance-based definition of outliers. In this work we assume that the moving objects’ location is uncertain, as the state of a moving object cannot be known precisely, and this uncertainty is modelled by the Gaussian distribution. For details please refer: MOOD: Moving Objects Outlier Detection (in APWeb2014).