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Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions

Karthik Ganesan Pillai, Rafal A. Angryk, Juan M. Banda, Michael A. Schuh, Tim Wylie
2012 2012 IEEE 12th International Conference on Data Mining Workshops  
We evaluate our framework on real-life data to demonstrate the effectiveness of our measures and the algorithm.  ...  We also propose a set of measures to identify spatio-temporal co-occurring patterns and propose an Apriori-based spatio-temporal cooccurrence mining algorithm to find prevalent spatio-temporal co-occurring  ...  We also plan to investigate new computationally efficient algorithms for mining spatio-temporal co-occurrence patterns.  ... 
doi:10.1109/icdmw.2012.130 dblp:conf/icdm/PillaiABSW12 fatcat:mlef7uqcnnc53dt6ypiny6htei

Prefetching Scheme for Massive Spatiotemporal Data in a Smart City

Lian Xiong, Zhengquan Xu, Hao Wang, Shan Jia, Li Zhu
2016 International Journal of Distributed Sensor Networks  
For massive spatiotemporal data, traditional pattern mining methods fail to directly reflect the spatiotemporal correlation and transition rules of user access, resulting in poor prefetching performance  ...  Further, the STAP scheme mines the user access patterns and constructs a predictive function to predict the user's next access request.  ...  Open-End Foundation of Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy (no.  ... 
doi:10.1155/2016/4127358 fatcat:rckp5alxtncl7ftxfefmhzibg4

Big Data New Frontiers: Mining, Search and Management of Massive Repositories of Solar Image Data and Solar Events [chapter]

Juan M. Banda, Michael A. Schuh, Rafal A. Angryk, Karthik Ganesan Pillai, Patrick McInerney
2014 Advances in Intelligent Systems and Computing  
This paper presents the current status of our work with solar image data and events, our shift towards using big data methodologies, and future directions for big data processing in solar physics.  ...  With over one terabyte of solar data being generated each day, and ever more missions on the horizon that expect to generate petabytes of data each year, solar physics presents many exciting opportunities  ...  While not all of our work is directly visual, such as high-dimensional indexing techniques and spatio-temporal frequent pattern mining [7, 11, 12] , almost all of it is related to some sort of visualizable  ... 
doi:10.1007/978-3-319-01863-8_17 fatcat:wroagiuelbclta5pp63dnu2s5a

Spatiotemporal Aspects of Big Data

Saadia Karim, Tariq Rahim Soomro, S. M. Aqil Burney
2018 Applied Computer Systems  
As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from traditional data.  ...  This study presents spatiotemporal aspects in big data with reference to several dissimilar environments and frameworks.  ...  Thus, spatiotemporal big data can create a strong framework that can help predict the future results based on the current knowledge.  ... 
doi:10.2478/acss-2018-0012 fatcat:rxp74qe4bvd4rk6bchql7ajaeu

A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets

Yan Huang, Liqin Zhang, Pusheng Zhang
2008 IEEE Transactions on Knowledge and Data Engineering  
Recent research literature has explored the sequential patterns on transaction data and trajectory analysis on moving objects.  ...  Two major research challenges still remain: 1) the definition of significance measures for spatio-temporal sequential patterns to avoid spurious ones and 2) the algorithmic design under the significance  ...  Vipin Kumar and Mr. Michael Steinbach from the University of Minnesota for their support in providing the climate data for our experimental evaluations.  ... 
doi:10.1109/tkde.2007.190712 fatcat:jfflrlxogzcq3es347tuxt3q3e

Transdisciplinary Foundations of Geospatial Data Science

Yiqun Xie, Emre Eftelioglu, Reem Ali, Xun Tang, Yan Li, Ruhi Doshi, Shashi Shekhar
2017 ISPRS International Journal of Geo-Information  
We also describe challenges and opportunities for future advancement. Based on the analysis, specific algorithm design paradigms can be explored for algorithm acceleration.  ...  Colocation pattern reveals geospatial events or objects that are frequently located within a close vicinity to each other (e.g., Nile Crocodiles and Egyptian Plover birds).  ...  Unlike association rule mining [36] in classic data mining, which finds frequent subsets of items in a given set of transactions, colocation pattern detection has to handle spatial point distributions  ... 
doi:10.3390/ijgi6120395 fatcat:w3uibnadrneqrctxgaw5dxfkqm

Spatiotemporal event sequence discovery without thresholds

Berkay Aydin, Soukaina Filali Boubrahimi, Ahmet Kucuk, Bita Nezamdoust, Rafal A. Angryk
2020 Geoinformatica  
We tested the relevance and performance of our threshold-free algorithm with a case study on solar event metadata, and compared the results with the previous STES mining algorithms.  ...  An STES is a spatiotemporal frequent pattern type, which is discovered from moving region objects whose polygon-based locations continiously evolve over time.  ...  of Astronomical Sciences within the Directorate for Mathematical and Physical Sciences, and the Division of Atmospheric and Geospace Sciences within the Directorate for Geosciences, under NSF award #1443061  ... 
doi:10.1007/s10707-020-00427-6 pmid:33192166 pmcid:PMC7649715 fatcat:uvsaaqyw2zclpmahml4owu2xee

Association Rules-Based Multivariate Analysis and Visualization of Spatiotemporal Climate Data

Feng Wang, Wenwen Li, Sizhe Wang, Chris Johnson
2018 ISPRS International Journal of Geo-Information  
Three techniques are introduced: (1) web-based spatiotemporal climate data visualization; (2) multiview and multivariate scientific data analysis; and (3) data mining-enabled visual analytics.  ...  Understanding atmospheric phenomena involves analysis of large-scale spatiotemporal multivariate data.  ...  other data mining algorithms, such as neutral network and deep learning.  ... 
doi:10.3390/ijgi7070266 fatcat:nf72gpftnvafbao6h7uxnq2o3e

Discovering spatiotemporal event sequences

Berkay Aydin, Rafal Angryk
2016 Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems - MobiGIS '16  
who contributed to my growth, both personally and professionally.  ...  Finally, I take this opportunity to thank all my good friends, especially Ezgi, Can, Feyyaz, Semih, Akif, Aytek, Evrim, Yasin, Oner, Fatih, and Deniz, who made my last five years more enjoyable.  ...  Zhipeng Cai, and Dr. Piet Martens, for their guidance and encouragement.  ... 
doi:10.1145/3004725.3004735 fatcat:3m7tehj3yjawdg2htdvuwn6guu

Big Data Reduction Methods: A Survey

Muhammad Habib ur Rehman, Chee Sun Liew, Assad Abbas, Prem Prakash Jayaraman, Teh Ying Wah, Samee U. Khan
2016 Data Science and Engineering  
patterns.  ...  It also presents a detailed taxonomic discussion of big data reduction methods including the network theory, big data compression, dimension reduction, redundancy elimination, data mining, and machine  ...  The authors of [79] proposed a map-reduce algorithm to reduce the search space and mine frequent patterns from uncertain big data.  ... 
doi:10.1007/s41019-016-0022-0 fatcat:3ivz52kpz5dhratokm4uenuoc4

A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example

Yongyao Jiang, Yun Li, Chaowei Yang, Fei Hu, Edward Armstrong, Thomas Huang, David Moroni, Lewis McGibbney, Frank Greguska, Christopher Finch
2018 ISPRS International Journal of Geo-Information  
In this article, we report a smart web-based geospatial data discovery system that mines and utilizes data relevancy from metadata user behavior.  ...  As a proof of concept, we focus on a well-defined domain-oceanography and use oceanographic data discovery as an example.  ...  The profile analyzer performs log mining and updates user access pattern in the knowledge base periodically.  ... 
doi:10.3390/ijgi7020062 fatcat:kaexmw2oqrf5pfezshkyxtc5au

Transport-domain applications of widely used data sources in the smart transportation: A survey [article]

Sina Dabiri, Kevin Heaslip
2018 arXiv   pre-print
Thirdly, a number of possible future research directions are provided for all types of data sources.  ...  social networks, 5) transit data with the focus on smart cards, and 6) environmental data.  ...  In the trajectory pattern extraction problems, frequent (sequential) patterns, which are the routes frequently followed by an object, are discovered for predicting the future movement using association  ... 
arXiv:1803.10902v3 fatcat:tc67qy4x4vbtjb76qi6mbwrqy4

Mining At Most Top-K% Spatiotemporal Co-occurrence Patterns in Datasets with Extended Spatial Representations

Karthik Ganesan Pillai, Rafal A. Angryk, Juan M. Banda, Dustin Kempton, Berkay Aydin, Petrus C. Martens
2016 ACM Transactions on Spatial Algorithms and Systems  
In this article, we focus our work on the problem of mining at most top-K% of STCOPs from continuously evolving spatiotemporal events that have polygon-like representations, without using a user-specified  ...  Finding STCOPs is an important problem in domains such as weather monitoring, wildlife migration, and solar physics.  ...  This is a problem common to all frequent pattern-mining algorithms, starting from shopping basket data, bio-sequence data, and ending at frequent subgraph mining.  ... 
doi:10.1145/2936775 fatcat:umeqgsqm5rhsnfyyvss6ylw5he

Spatio-Temporal Data Mining: A Survey of Problems and Methods [article]

Gowtham Atluri, Anuj Karpatne, Vipin Kumar
2017 arXiv   pre-print
Based on the nature of the data mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern  ...  mining, anomaly detection, and relationship mining.  ...  Frequent Pattern Mining Frequent pattern mining is the process of discovering patterns in a data set that occur frequently over multiple instances in a data set, e.g., frequently bought groups of items  ... 
arXiv:1711.04710v2 fatcat:di3fxigwobeb3db5kcdvlhbe7i

A filter-and-refine approach to mine spatiotemporal co-occurrences

Karthik Ganesan Pillai, Rafal A. Angryk, Berkay Aydin
2013 Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL'13  
We provide theoretical analysis of our approach, and follow this investigation with a practical evaluation of our algorithm effectiveness on three real-life data sets and one artificial data set.  ...  Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of event types that occur together in both space and time.  ...  Acknowledgment This work was supported by two National Aeronautics and Space Administration (NASA) grant awards, 1) No. NNX09AB03G and 2) No. NNX11AM13A.  ... 
doi:10.1145/2525314.2525367 dblp:conf/gis/PillaiAA13 fatcat:jskgjujqjngxnfcffkpi3agidi
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