Location based Continuous Query Processing over Geo-streaming Data

In recent years, many data-intensive and location based applications have emerged that need to process stream data in applications such as network monitoring, telecommunications data management, and sensor networks. Unlike regular queries, a continuous query exists for certain period of time and need to be continuously processed during this time. The algorithms used for data processing for the traditional database systems are not suited to tackle complex and various continuous queries over dynamic streaming data. The indexing for finite queries is preferred to indexing on infinite data to avoid expensive operations of index maintenance. Previous related work focused on moving queries on static objects or static queries on moving object. But now-a-days queries as well as objects are dynamic. So, hybrid indexing for queries significantly reduces the space costs and scales well with the increasing data. To deal with the speed of unbounded data, it is necessary to use data parallelism in query processing. The data parallelism in query processing offers better performance, availability and scalability.

A Secure Information Discovery Using Mobile Agents in Wireless Industry 4.0 Networks

Industry 4.0 became the boon to secure information discovery. In most existing information discovery systems, mobile computing processes are complex due to their continuous query processing overhead to provide “right information at right time.” Even though many searching applications are there, they are unable to provide the accurate information when needed as the required objects are not being updated regularly. This paper intends to provide a process and Industry 4.0 architectures, which performs a secure information and discovery database through securing the users query. The paper also deals with the responses to the respective query made by monitor tracker agent. The research work secures the information discovery using mobile agents in wireless Industry 4.0 networks.

DESERT: A Continuous SPARQL Query Engine for On-Demand Query Answering

The Internet of Things (IoT) has been rapidly adopted in many domains ranging from household appliances e.g. ventilation, lighting, and heating, to industrial manufacturing and transport networks. Despite the, enormous benefits of optimization, monitoring, and maintenance rendered by IoT devices, an ample amount of data is generated continuously. Semantically describing IoT generated data using ontologies enables a precise interpretation of this data. However, ontology-based descriptions tremendously increase the size of IoT data and in presence of repeated sensor measurements, a large amount of the data are duplicates that do not contribute to new insights during query processing or IoT data analytics. In order to ensure that only required ontology-based descriptions are generated, we devise a knowledge-driven approach named DESERT that is able to on-[Formula: see text]emand factoriz[Formula: see text] and [Formula: see text]emantically [Formula: see text]nrich st[Formula: see text]eam da[Formula: see text]a. DESERT resorts to a knowledge graph to describe IoT stream data; it utilizes only the data that is required to answer an input continuous SPARQL query and applies a novel method of data factorization to reduce duplicated measurements in the knowledge graph. The performance of DESERT is empirically studied on a collection of continuous SPARQL queries from SRBench, a benchmark of IoT stream data and continuous SPARQL queries. Furthermore, data streams with various combinations of uniform and varying data stream speeds and streaming window size dimensions are considered in the study. Experimental results suggest that DESERT is capable of speeding up continuous query processing while creates knowledge graphs that include no replications.

Data Stream of Wireless Sensor Networks Based on Deep Learning

The sensor data in wireless sensor networks are continuously arriving in multiple, rapid, time varying, possibly unpredictable, unbounded streams, and no record of historical information is kept. These limitations make conventional Database Management Systems and their evolution unsuitable for streams. Thereby there is a need to build a complete Data Streaming Management System (DSMS), which could process streams and perform dynamic continuous query processing. In this paper, a framework for Adaptive Distributed Data Streaming Management System (ADDSMS) is presented, which operates as streams control interface between arrays of distributed data stream sources and end-user clients who access and analyze these streams. Simulation results show that the proposed method can thus improve overall system performance substantially.

Answering Continuous Description Logic Queries

During the last years, mobile computing has been the focus of many research efforts, due mainly to the ever-growing use of mobile devices. In this context, there is a need to manage dynamic data, such as location data or other data provided by sensors. As an example, the continuous processing of location-dependent queries has been the subject of thorough research. However, there is still a need of highly expressive ways of formulating queries, augmenting in this way the systems' answer capabilities. Regarding this issue, the modeling power of Description Logics (DLs) and the inferring capabilities of their attached reasoners could fulfill this new requirement. The main problem is that DLs are inherently oriented to model static knowledge, that is, to capture the nature of the modeled objects, but not to handle changes in the property values (which requires a full ontology reclassification), as it is common in mobile computing environments (e.g., the location is expected to vary continually). In this paper, the authors present a novel approach to process continuous queries that combines 1) the DL reasoning capabilities to deal with static knowledge, with 2) the efficient data access provided by a relational database to deal with volatile knowledge. By marking at modeling time the properties that are expected to change during the lifetime of the queries, the authors'system is able to exploit both the results of the classification process provided by a DL reasoner, and the low computational costs of a database when accessing changing data (mobile environments, semantic sensors, etc.), following a two-step continuous query processing that enables us to handle continuous DL queries efficiently. Experimental results show the feasibility of the authors' approach.