{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:42:19Z","timestamp":1754156539611,"version":"3.41.2"},"reference-count":31,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2017,6,12]],"date-time":"2017-06-12T00:00:00Z","timestamp":1497225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2017,6,12]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>First, the autoregressive integrated moving average-based prediction algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijicc-09-2016-0033","type":"journal-article","created":{"date-parts":[[2017,4,28]],"date-time":"2017-04-28T07:40:08Z","timestamp":1493365208000},"page":"145-165","source":"Crossref","is-referenced-by-count":4,"title":["A distributed real-time data prediction framework for large-scale time-series data using stream processing"],"prefix":"10.1108","volume":"10","author":[{"given":"Kehe","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yayun","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziwei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"10","key":"key2020120705125524200_ref001","doi-asserted-by":"crossref","first-page":"3651","DOI":"10.1007\/s11269-015-1021-z","article-title":"Urban residential water demand prediction based on artificial neural networks and time series models","volume":"29","year":"2015","journal-title":"Water Resources Management"},{"key":"key2020120705125524200_ref002","unstructured":"ASF (2016a), \u201cApache Hadoop\u201d, available at: http:\/\/hadoop.apache.org\/ (accessed September 13, 2016)."},{"key":"key2020120705125524200_ref003","unstructured":"ASF (2016b), \u201cApache HBase\u201d, available at: http:\/\/hbase.apache.org\/ (accessed September 13, 2016)."},{"key":"key2020120705125524200_ref004","unstructured":"ASF (2016c), \u201cApache Spark Streaming\u201d, available at: http:\/\/spark.apache.org\/streaming\/ (accessed September 13, 2016)."},{"key":"key2020120705125524200_ref005","unstructured":"ASF (2016d), \u201cApache Storm\u201d, available at: http:\/\/storm.apache.org\/ (accessed September 13, 2016)."},{"issue":"1","key":"key2020120705125524200_ref006","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TSG.2010.2044899","article-title":"Smart transmission grid applications and their supporting infrastructure","volume":"1","year":"2010","journal-title":"IEEE Transactions on Smart Grid"},{"edition":"3rd ed.","volume-title":"Time Series Analysis: Forecasting and Control","year":"2013","key":"key2020120705125524200_ref007"},{"issue":"5","key":"key2020120705125524200_ref008","doi-asserted-by":"crossref","first-page":"2426","DOI":"10.1109\/TSG.2015.2402224","article-title":"Preventing occupancy detection from smart meters","volume":"6","year":"2015","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"10","key":"key2020120705125524200_ref009","first-page":"1053","article-title":"Time series forecasting for nonlinear and non-stationary processes: a review and comparative study","volume":"47","year":"2015","journal-title":"IIE Transactions (Institute of Industrial Engineers)"},{"key":"key2020120705125524200_ref010","doi-asserted-by":"crossref","unstructured":"Cui, H., Keeton, K., Roy, I., Viswanathan, K. and Ganger, G.R. (2015), \u201cUsing data transformations for low-latency time series analysis\u201d, HP Laboratories Technical Report, No. 74, Palo Alto, CA.","DOI":"10.1145\/2806777.2806839"},{"issue":"1","key":"key2020120705125524200_ref011","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TC.2013.198","article-title":"Time series characterization of gaming workload for runtime power management","volume":"64","year":"2015","journal-title":"IEEE Transactions on Computers"},{"issue":"2","key":"key2020120705125524200_ref012","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s11334-015-0270-6","article-title":"Run-time monitoring using bounded constraint instance discovery within big data streams","volume":"12","year":"2016","journal-title":"Innovations in Systems and Software Engineering"},{"key":"key2020120705125524200_ref013","unstructured":"EIA, US (2014), \u201cHow many smart meters are installed in the US and who has them?\u201d, Frequently asked questions, available at: www.eia.gov\/tools\/faqs\/faq.cfm?id=108&t=3 (accessed September 13, 2016)."},{"issue":"9","key":"key2020120705125524200_ref014","first-page":"909","article-title":"On-condition maintenance of wind generators \u2013 from prediction algorithms to hardware for data acquisition and transmission","volume":"7","year":"2008","journal-title":"WSEAS Transactions on Circuits and Systems"},{"issue":"1","key":"key2020120705125524200_ref015","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.engappai.2010.09.007","article-title":"A review on time series data mining","volume":"24","year":"2011","journal-title":"Engineering Applications of Artificial Intelligence"},{"first-page":"2040","article-title":"Real time analytics: algorithms and systems","year":"2006","key":"key2020120705125524200_ref016"},{"first-page":"24","article-title":"A probabilistic approach to fast pattern matching in time series databases","year":"1997","key":"key2020120705125524200_ref017"},{"first-page":"1","article-title":"Economic prediction using heterogeneous data stremas from the world wide web","year":"2013","key":"key2020120705125524200_ref018"},{"issue":"1","key":"key2020120705125524200_ref019","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TKDE.2007.190666","article-title":"Efficient similarity search over future stream time series","volume":"20","year":"2008","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"key2020120705125524200_ref020","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.ymssp.2012.05.004","article-title":"A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential Monte Carlo methods","volume":"32","year":"2012","journal-title":"Mechanical Systems and Signal Processing"},{"first-page":"309","article-title":"A scalable distributed stream mining system for highway traffic data","year":"2006","key":"key2020120705125524200_ref021"},{"first-page":"1","article-title":"Grid analytics: how much data do you really need?","year":"2013","key":"key2020120705125524200_ref022"},{"issue":"1","key":"key2020120705125524200_ref023","first-page":"180","article-title":"Outlier detection in structural time series models: the indicator saturation approach","volume":"32","year":"2016","journal-title":"SSRN Electronic Journal"},{"first-page":"728","article-title":"A new algorithm for time series prediction by temporal fuzzy clustering","year":"2000","key":"key2020120705125524200_ref024"},{"key":"key2020120705125524200_ref025","unstructured":"RaspberryPIFoundation (2015), \u201cRaspberryPI\u201d, available at: www.raspberrypi.org\/ (accessed September 13, 2016)."},{"first-page":"787","article-title":"Causality quantification and its applications: structuring and modeling of multivariate time series","year":"2009","key":"key2020120705125524200_ref026"},{"first-page":"125","article-title":"Real-time anomaly detection from environmental data streams","year":"2015","key":"key2020120705125524200_ref027"},{"issue":"1","key":"key2020120705125524200_ref028","first-page":"135","article-title":"A review of DAN2 (dynamic architecture for artificial neural networks) model in time series forecasting","volume":"16","year":"2012","journal-title":"Ingenieria y Universidad"},{"issue":"3","key":"key2020120705125524200_ref029","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1108\/17563781111159996","article-title":"Intelligent techniques for forecasting multiple time series in real-world systems","volume":"4","year":"2011","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"key":"key2020120705125524200_ref030","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/ACCESS.2015.2500258","article-title":"A hybrid processing system for large-scale traffic sensor data","volume":"3","year":"2015","journal-title":"IEEE Access"},{"first-page":"358","article-title":"StatStream: statistical monitoring of thousands of data streams in real time","year":"2002","key":"key2020120705125524200_ref031"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-09-2016-0033\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-09-2016-0033\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:54:52Z","timestamp":1753397692000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/10\/2\/145-165\/119037"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,12]]},"references-count":31,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,6,12]]}},"alternative-id":["10.1108\/IJICC-09-2016-0033"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-09-2016-0033","relation":{},"ISSN":["1756-378X"],"issn-type":[{"type":"print","value":"1756-378X"}],"subject":[],"published":{"date-parts":[[2017,6,12]]}}}