A Graphical Conceptual Model for Conventional and Time-varying JSON Data

Today, although there is an increasing interest in temporal JSON instance documents, since they allow tracking data changes, recovering past data versions, and executing temporal queries, there is no support (data model, modelling language, method, or tool) for conceptual modelling of temporal JSON data. Moreover, even though there are some graphical editors to build JSON Schemata (like JSON Schema Editor of Altova), they do not provide any built-in support for modelling temporal aspects of JSON data. Therefore, designers of JSON-based NoSQL data stores are proceeding in an ad hoc manner when they have to model some temporal requirements. To fill this theoretical and practical gap, we propose in this paper a graphical conceptual model for time-varying JSON data, named Temporal JSON Conceptual Model (TempoJCM). To this purpose, first we define a graphical conceptual model for conventional (i.e., non temporal) JSON data, called JSON Conceptual Model (JCM), and then we extend it to support modelling of temporal aspects of JSON data. TempoJCM facilitates conceptual modelling of both conventional and temporal JSON data, in a graphical and user-friendly manner. An editor supporting TempoJCM is planned to become the user interface for temporal JSON schema design in the tauJSchema framework

A Graphical Conceptual Model for Conventional and Time-varying JSON Data

Abstract Today, although there is an increasing interest in temporal JSON instance documents, since they allow tracking data changes, recovering past data versions, and executing temporal queries, there is no support (data model, modelling language, method, or tool) for conceptual modelling of temporal JSON data. Moreover, even though there are some graphical editors to build JSON Schemata (like JSON Schema Editor of Altova), they do not provide any built-in support for modelling temporal aspects of JSON data. Therefore, designers of JSON-based NoSQL data stores are proceeding in an ad hoc manner when they have to model some temporal requirements. To fill this theoretical and practical gap, we propose in this paper a graphical conceptual model for time-varying JSON data, named Temporal JSON Conceptual Model (TempoJCM). To this purpose, first we define a graphical conceptual model for conventional (i.e., non temporal) JSON data, called JSON Conceptual Model (JCM), and then we extend it to support modelling of temporal aspects of JSON data. TempoJCM facilitates conceptual modelling of both conventional and temporal JSON data, in a graphical and user-friendly manner. An editor supporting TempoJCM is planned to become the user interface for temporal JSON schema design in the τJSchema framework

Modeling temporal dimensions of semistructured data

In this paper we propose an approach to manage in a correct way valid time semantics for semistructured temporal clinical information. In particular, we use a graph-based data model to represent radiological clinical data, focusing on the patient model of the well known DICOM standard, and define the set of (graphical) constraints needed to guarantee that the history of the given application domain is consistent

6 Access Methods and Query Processing Techniques

The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B +-tree, hashing with chaining, and, in som

Challenging Issues of Spatio-Temporal Data Mining

The spatio-temporal database (STDB) has received considerable attention during the past few years, due to the emergence of numerous applications (e.g., flight control systems, weather forecast, mobile computing, etc.) that demand efficient management of moving objects. These applications record objects' geographical locations (sometimes also shapes) at various timestamps and support queries that explore their historical and future (predictive) behaviors. The STDB significantly extends the traditional spatial database, which deals with only stationary data and hence is inapplicable to moving objects, whose dynamic behavior requires re-investigation of numerous topics including data modeling, indexes, and the related query algorithms. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we have presented the challenging issues of spatio-temporal data mining. Keywords: database, data mining, spatial, temporal, spatio-tempora

An Efficient and Robust Tuple Timestamp Hybrid Historical Relational Data Model

This paper proposes a novel, efficient and robust tuple time stamped hybrid historical relational model for dealing with temporal data. The primary goal of developing this model is to make it easier to manage historical data robustly with minimal space requirements and retrieve it more quickly and efficiently. The model's efficiency and results were revealed when it was applied to an employee database. The proposed model's performance in terms of query execution time and space requirements is compared to a single relational data model. The obtained results show that the proposed model is approximately 20% faster than the conventional single relational data model. Memory consumption results also show that the proposed model's memory cost at different frequencies is significantly reduced, which is approximately 30% less than the single relational data model for a set of queries. Because net cost is strongly related to query execution time and memory cost, the suggested model's net cost is also significantly reduced. The proposed tuple timestamp hybrid historical model acts as generic, accurate and robust model. It provides the same functionality as previous versions, as well as hybrid functionality of previously proposed models, with a significant improvement in query execution speed and memory usage. This model is effective and reliable for the use in a wide range of temporal database fields, including insurance, geographic information systems, stocks and finance (e.g. Finacle in Banking), data warehousing, scientific databases, legal case histories, and medical records