A framework for co-location patterns mining in big spatial data

Garaeva, Alina; Makhmutova, F.; Anikin, Igor V.; Sattler, Kai-Uwe

doi:10.1109/scm.2017.7970622

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…Cited research works location event log preparation and interpretation (Senderovich et al, 2016;Muzammal et al, 2018;Wan et al, 2017;Namaki Araghi et al, 2018a) Use of location data for extraction of knowledge (Hwang and Jang, 2017;Sztyler et al, 2016;Ertek et al, 2017;Mazimpaka and Timpf, 2016), (Rojas et al, 2017a;Zheng, 2015a;Tanuja and Govindarajulu, 2017;Ramos et al, 2017;Garaeva et al, 2017;Bao and Wang, 2017), (Zhenjiang et al, 2017;Aryal and Sujing Wang, 2017;Tanuja and Govindarajulu, 2016;Feng Ling et al, 2016), (Lamr and Skrbek, 2016;Blank et al, 2016;Fernandez-Llatas et al, 2015;Zheng, 2015b), (Liao et al, 2015;Tang et al, 2015;Miclo et al, 2015;Jin et al, 2015;Martinez-Millana et al, 2019), (Dogan et al, 2019;Namaki Araghi et al, 2019;Namaki Araghi et al, 2018a;Namaki Araghi et al, 2018b;Araghi et al, 2018) Figure 3: Analysis of the literature of process mining, indoor localization systems, and data mining relevant to the approaches in which the location data were used.…”

Section: Preparation and Interpretation Of Location Datamentioning

confidence: 99%

Interpretation of Patients’ Location Data to Support the Application of Process Mining Notations

Araghi

Fontanili

Lamine

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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The application of indoor localization and process mining emerges as an intriguing tool for the researchers to address the structural issues related to the patient pathways inside healthcare organizations. However, there is a major gap in the literature. This is related to the lack of enough attention to the interpretation of location data. Therefore, as a contribution, this article presents the DIAG meta-model and relevant location data interpretation rules. This model-driven approach has been realized in the context of the R-IOSUITE application and it supports the further analyses by the process mining methods.

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Section: Preparation and Interpretation Of Location Datamentioning

confidence: 99%

Interpretation of Patients’ Location Data to Support the Application of Process Mining Notations

Araghi

Fontanili

Lamine

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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“…These queries are used in various applications and services such as social recommendations (Zhang & Chow 2015;Hammou et al 2018), social community and event detection (Al Aghbari et al 2019;Sapountzi & Psannis 2018), wireless sensor networks (Al Aghbari et al 2012Aghbari et al , 2013, image analysis (Dinges et al 2011;Kubo et al 2003), smart cities (Hanif et al 2018;Alsaafin et al 2018), and urban route planning Babar et al (2019). As a result, spatial data mining has emerged, which deals with gaining knowledge, forming spatial relations, and mining interesting trends and patterns from geo-tagged data (Al Jawarneh et al 2018;Garaeva et al 2017).…”

Section: Introductionmentioning

confidence: 99%

SparkNN: A Distributed In-Memory Data Partitioning for KNN Queries on Big Spatial Data

Aghbari

Ismail

Kamel

2020

Data Science Journal

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The increase in GPS-enabled devices and proliferation of location-based applications have resulted in an abundance of geotagged (spatial) data. As a consequence, numerous applications have emerged that utilize the spatial data to provide different types of location-based services. However, the huge amount of available spatial data presents a challenge to the efficiency of these location-based services. Although the advent of big data frameworks like Apache Spark has enabled the processing of large amounts of data efficiently, they are designed for general (non-spatial) data. That is due to the build-in data partitioning mechanism that does not take into account the spatial proximity of the data. Therefore, these big data frameworks cannot be readily used for spatial analytics such as efficiently answering spatial queries. To fill this gap, this paper proposes SparkNN, an in-memory partitioning and indexing system for answering spatial queries, such as K-nearest neighbor, on big spatial data. SparkNN is implemented on top of Apache Spark and consists of three layers to facilitate efficient spatial queries. The first layer is a spatial-aware partitioning layer, which partitions the spatial data into several partitions ensuring that the load of the partitions is balanced and data objects with close proximity are placed in the same, or neighboring, partitions. The second layer is a local indexing layer, which provides a spatial index inside each partition to speed up the data search within the partition. The third layer is a global index, which is placed in the master node of Spark to route spatial queries to the relevant partitions. The efficiency of SparkNN was evaluated by extensive experiments with big spatial datasets. The results show SparkNN significantly outperforms the state-of-the-art Spark system when evaluated on the same set of queries.

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“…Modern EMIS also contains a text description of the event and possible event's category defined by operators. Specific nature of data in EMIS demands to use particular spatio-temporal data mining algorithms [2,3]. These algorithms mainly focus on obtaining explicit knowledge, spatial and temporal relations and interesting patterns from spatio-temporal components of data.…”

Section: Introductionmentioning

confidence: 99%