感知传感器网络中发现传感器信息的图形化查询生成器
Graphical Query Builder in Opportunistic Sensor Networks to discover Sensor Information
摘要：
许多传感器网络应用都是数据驱动的。我们认为查询是发现传感器服务的最佳方式。通常用户不知道可用的传感器。因此，用户需要通过传感器网络提出不同类型的查询，以获得所需的信息。甚至用户可能需要输入具有更高聚合级别的更复杂查询，并且需要与系统进行更复杂的交互。由于用户没有传感器数据或服务的先验知识，我们的目标是开发一个可视化的查询界面，用户可以提供更多的用户友好的查询和机器可以理解这些。在本文的工作中，我们已经开发了一个交互式的可视化查询界面的用户。为了实现这一点，我们已经考虑了几个用例，我们已经从这些用例场景的基于文本的格式中导出了查询的图形表示。通过从Ontology中抽取类、子类和属性，方便了用户。为此，我们在用户界面中解析了OWL文件，并根据解析的信息用户构建可视化查询。后来，我们已经将可视化的查询语言翻译成SPARQL查询，这是一种机器可理解的格式，有助于机器与底层技术进行通信。
英文摘要：
A lot of sensor network applications are data-driven. We believe that query is the most preferred way to discover sensor services. Normally users are unaware of available sensors. Thus users need to pose different types of query over the sensor network to get the desired information. Even users may need to input more complicated queries with higher levels of aggregations, and requires more complex interactions with the system. As the users have no prior knowledge of the sensor data or services our aim is to develop a visual query interface where users can feed more user friendly queries and machine can understand those. In this paper work, we have developed an Interactive visual query interface for the users. To accomplish this we have considered several use cases and we have derived graphical representation of query from their text based format for those use case scenario. We have facilitated the user by extracting class, subclass and properties from Ontology. To do so we have parsed OWL file in the user interface and based upon the parsed information users build visual query. Later on we have translated the visual query languages into SPARQL query, a machine understandable format which helps the machine to communicate with the underlying technology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
