Semantic data model

Résumé

A semantic data model (SDM) is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An SDM specification describes a database in terms of the kinds of entities that exist in the application environment, the classifications and groupings of those entities, and the structural interconnections among them. SDM provides a collection of high-level modeling primitives to capture the semantics of an application environment. By accommodating derived information in a database structural specification, SDM allows the same information to be viewed in several ways; this makes it possible to directly accommodate the variety of needs and processing requirements typically present in database applications. The design of the present SDM is based on our experience in using a preliminary version of it. SDM

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https://en.wikipedia.org/wiki/Semantic_data_model

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EPFL2003

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Robust and Hierarchical Stop Discovery in Sparse and Diverse Trajectories

Ngoc Hoan Do, Quoc Viet Hung Nguyen, Le Hung Tran, Zhixian Yan

The advance of GPS tracking technique brings a large amount of trajectory data. To better understand such mobility data, semantic models like “stop/move” (or inferring “activity”, “transportation mode”) recently become a hot topic for trajectory data analysis. Stops are important parts of tra- jectories, such as “working at office”, “shopping in a mall”, “waiting for the bus”. There are several methods such as velocity, clustering, density algorithms being designed to discover stops. However, existing works focus on well-defined trajectories like movement of vehicle and taxi, not working well for heterogeneous cases like diverse and sparse trajectories. On the contrary, our paper addresses three main challenges: (1) provide a robust clustering-based method to discover stops; (2) discover both shared stops and personalized stops, where shared stops are the common places where many trajectories pass and stay for a while (e.g. shopping mall), whilst personalized stops are individual places where user stays for his/her own purpose (e.g. home, office); (3) further build stop hierarchy (e.g. a big stop like EPFL campus and a small stop like an office building). We evaluate our approach with several diverse and spare real-life GPS data, compare it with other methods, and show its better data abstraction on trajectory.

2011

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The International Workshop on Semantic Data Management (SemData) was held in Singapore co-located with the VLDB Conference 2010, with the goal of serving as a platform for the discussion and investigation of various aspects related to semantic databases and data management in the large. The workshop was a full-day event featuring two research sessions, one industry session and a panel discussion, and attracted over 25 attendees. This report summarizes the key topics presented, interesting ideas discussed and the new perspectives identified during the workshop.(1)

Association for Computing Machinery2010

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