Spatiotemporal Data Warehousing for Event Tracking Applications

Serhat Peker Gizem Turcan

1 Desember 2022

The Covid-19 pandemic has brought about a new lifestyle for across the globe. Throughout this period, the use of holistic methods has become indispensable to deal with the enormous amount of data in this regard. It appears that the simplest way to tackle this issue is to spread the digitalization efforts concerning all data-based applications. Given the significance of pandemic data management, it is essential to have a data warehouse that collects, associates, and communicates these data. Containing a significant volume of structured data, warehousing can provide the necessary foundation for data mining and the development of analytical tools. To this end, the present paper proposes a data warehouse for combatting and managing pandemics, with the possibility to be enhanced for other personal or public health-related initiatives. In this research, the bottom-up data warehouse building methodology is used to construct a warehouse. A fact constellation schema model is utilized to accommodate the information ranging from citizen demographics to physician-prescribed drugs and laboratory tests. Sample queries are executed based on the proposed data warehouse for different purposes, and desired query results are obtained within proper response times. The proposed data warehouse contributes to countrywide implementation of pandemic practices and illuminates research on faster, less expensive, and safer management of citywide, nationwide, or worldwide health emergencies within a robust technical framework by governments.

A. Vaisman E. Zimányi R. Wrembel + 1 lainnya

1 Oktober 2020

Usually, data in data warehouses (DWs) are stored using the notion of the multidimensional (MD) model. Often, DWs change in content and structure due to several reasons, like, for instance, changes in a business scenario or technology. For accurate decision-making, a DW model must allow storing and analyzing time-varying data. This paper addresses the problem of keeping track of the history of the data in a DW. For this, first, a formalization of the traditional MD model is proposed and then extended as a generalized temporal MD model. The model comes equipped with a collection of typical online analytical processing (OLAP) operations with temporal semantics, which is formalized for the four classic operations, namely roll-up, dice, project, and drill-across. Finally, the mapping from the generalized temporal model into a relational schema is presented together with an implementation of the temporal OLAP operations in standard SQL.

Michael Mireku Kwakye

1 Juli 2020

The trajectory patterns of a moving object in a spatio-temporal domain offers varied information in terms of the management of the data generated from the movement. The query results of trajectory objects from the data warehouse are usually not enough to answer certain trend behaviours and meaningful inferences without the associated semantic information of the trajectory object or the geospatial environment within a specified purpose or context. This article formulates and designs a generic ontology modelling framework that serves as the background model platform for the design of a semantic data warehouse for trajectories. The methodology underpins on higher granularity of data as a result of pre-processed and extract-transformed-load (ETL) data so as to offer efficient semantic inference to the underlying trajectory data. Moreover, the modelling approach outlines the thematic dimensions that offer a design platform for predictive trend analysis and knowledge discovery in the trajectory dynamics and data processing for moving objects.

J. Akaichi Noura Azaiez

1 Oktober 2020

Business Intelligence includes the concept of data warehousing to support decision making. As the ETL process presents the core of the warehousing technology, it is responsible for pulling data out of the source systems and placing it into a data warehouse. Given the technology development in the field of geographical information systems, pervasive systems, and the positioning systems, the traditional warehouse features become unable to handle the mobility aspect integrated in the warehousing chain. Therefore, the trajectory or the mobility data gathered from the mobile object movements have to be managed through what is called the trajectory ELT. For this purpose, the authors emphasize the power of the model-driven architecture approach to achieve the whole transformation task, in this case transforming trajectory data source model that describes the resulting trajectories into trajectory data mart models. The authors illustrate the proposed approach with an epilepsy patient state case study.

Yuya Sasaki

15 Januari 2022

The Internet of Things (IoT) has become widespread around the world. Since a large number of diverse devices, such as vehicles, household electrical appliances, smart phones, and environmental sensors are connected to the Internet, we can obtain a large volume of diverse IoT data, known as IoT big data. The generation of IoT big data means that efficient analytic systems are needed for many application scenarios, for example, to optimize urban planning, solve air pollution problems, and improve business decisions. In this survey, we review current systems that can efficiently analyze IoT data. Existing systems can be categorized into batch and stream processing systems. We explore Hadoop- and Spark-based batch processing systems for spatiotemporal and trajectory data. We also review fog- and edge-aware stream processing systems. Although many existing systems can efficiently and effectively analyze specific data and tasks, no system exists that can handle all characteristics of IoT big data: volume, velocity, variety, veracity, and variability. We present some open issues and discuss the future of IoT big data analytic systems. This survey aims to help researchers and practitioners better understand current systems and develop new IoT big data analytic systems.