SENSEI: Software evolution service integration

Abstract

Abstract-Software evolution tools mostly implement a single technique to assist in achieving a specific objective. Overhauling, renovating, or migrating large and complex legacy software systems require the proper combination of several different techniques appropriate for each subtask. Since few tools are built for interoperability, the setup of a toolchain supporting a given software evolution process is an elaborate, time-consuming, errorprone, and redundant endeavor, which yields brittle and inflexible toolchains with little to no reusability. This paper presents SENSEI, an approach to enable the implementation of an integration framework for software evolution tools using component-based, service-oriented, and model-driven methods, to ease toolchain creation and enable agile execution of software evolution projects. It will be evaluated by implementing and using it to build the toolchains supporting two software evolution projects, and having practitioners assess its usefulness.

Semantic Integration of Sensor Data with SSN Ontology in a Multi-Agent Architecture for Intelligent Transportation Systems

“…Most of them have emerged in the field of linked sensor data, such as Esk Hydrological sensor network [9] and SENSEI [10], which focused on the transformation of the sensor data to linked sensor. The study of the semantic sensor network has been also applied in other research fields, for example, the semantic medical monitoring system model [11] based.…”

A Method for Mapping Sensor Data to SSN Ontology

“…According to the tablename, the observation resultset rs can be obtained (line 4). In line 5, we get the OntClass set and OntProperty set from SSN ontology model according to the correspondence list L. Then for each sensor, we create individual I sensor as well as the annotation individuals I annotation , and connect them using corresponding Object Property (line [6][7][8][9][10][11] (line17-18). Finally, the sensor data in database is transformed into the instances of SSN ontology and stored in OWL file.…”

“…Currently, a lot of applications have emerged in the field of linked sensor data. For example, Esk Hydrological sensor network [10] and SENSEI [11] have done abundant work on the transformation of the sensor data to linked sensor data [12][13][14]. In many other fields, the study of the semantic sensor network is also indispensable, e.g., the semantic medical monitoring system model [15] based.…”