데이터 스트림 상에서 다중 연속 질의 처리를 위한 속성기반 접근 기법

Data stream management systems (DSMSs) are conceived for running continuous queries (CQs) on the most recently streamed data. This model does not completely fit the needs of several modern data-intensive applications that require to manage recent/historical/static data and execute both CQs and OTQs joining such data. In order to cope with these new needs, some DSMSs have moved toward the integration of database management systems (DBMSs) functionalities to augment their capabilities. In this paper we adopt the opposite perspective and we lay the groundwork for extending DBMSs to natively support streaming facilities. To this end, we introduce a new kind of table, the streaming table , as a persistent structure where streaming data enters and remains stored for a long period, ideally forever. Streaming tables feature a novel access paradigm: continuous writes and one-time as well as continuous reads. We present a streaming table implementation and two novel types of indices that efficiently support both update and scan high rates. A detailed experimental evaluation shows the effectiveness of the proposed technology.

In recent years, data stream technology has been widely used in the processing of sensor data. Sonar sensors produce high-speed streaming data with special characteristics of huge volume continuously. Therefore, general data stream management systems with limited memory and processor resources leads to inefficiencies in processing this kind of sonar data stream. However, in distributed data stream management system (DDSMS), continuous query can be reduced into several steps and processed on different processing nodes efficiently and effectively, so DDSMS is often adopted to deal with sonar stream data. Load balancing is a key technology of DDSMS, existing researches mainly focus on using computing load mainly caused by processing queries on the node as the key factor of load balancing algorithm, ignoring the load of transmitting huge volume data mainly caused by moving data among nodes. In this paper, the transmission load of sonar data is computed and used in an improved load balancing algorithm, which is proposed to solve the problem of load scheduling for sonar stream data processing under the distributed environment. The simulation verifies the importance of considering transmission load and shows a better performance of improved load balancing algorithm.

Because of increased stream data, managing stream data has become quite important. This paper describes our data stream management system, which employs an architecture combining a stream processing engine and DBMS. Based on the architecture, the system processes both continuous queries and traditional one-shot queries. Our proposed query language supports not only filtering, join, and projection over data streams, but also continuous persistence requirements for stream data. Users can also specify continuous queries that integrate streaming data and historical data stored in DBMS. Another contribution of this paper is feasibility validation of queries. Processing queries on streams with frequent inputs may cause the system to overflow its capacity. Specifically, the maximum writing rate to DBMS is a significant bottleneck when we try to store stream data into DBMS. Our system detects infeasible queries in advance.

Energy Management Systems (EMSs) are used to monitor energy consumption in buildings with the purpose of improving energy efficiency, by identifying savings opportunities and misuse situations. To achieve that, an EMS collects energy metering data streams from a network of energy meters in the building. Sensor data must be processed in (near) real-time, to support a timely decision making process. Currently, EMSs are using traditional DBMSs to process such data, introducing a persistence step that translates to an unacceptable latency on data evaluation. Moreover, sensor data monitoring queries are not elegantly supported by the SQL query language, thus hampering the ability of an EMS to process energy metering data in realtime. Data Stream Management Systems (DSMSs) are used to process data streams efficiently in several domains. Many sensor network monitoring applications have been implemented upon DSMSs resulting in significant improvements on performance and overall resource usage. This work validates the hypothesis proposed in [1] that, to process energy metering data streams in realtime, EMSs should be supported by DSMSs, instead of DBMSs. We introduce an EMS’s Data Processing Architecture supported by a DSMS that supports the implementation of an EMS capable of performing real-time data processing. We validate our solution through a benchmark evaluation against a DBMS based architecture. The results clearly show that the DSMSbased EMS outperformed the state of the art approach, both in data evaluation latency and query language expressibility— demonstrating its adequacy to process energy metering data streams in real-time.

Most data stream management systems are based on extensions of the relational data model and query languages, but rigorous analyses of the problems and limitations of this approach, and how to overcome them, are still wanting. In this article, we elucidate the interaction between stream-oriented extensions of the relational model and continuous query language constructs, and show that the resulting expressive power problems are even more serious for data streams than for databases. In particular, we study the loss of expressive power caused by the loss of blocking query operators, and characterize nonblocking queries as monotonic functions on the database. Thus we introduce the notion of NB -completeness to assure that a query language is as suitable for continuous queries as it is for traditional database queries. We show that neither RA nor SQL are NB -complete on unordered sets of tuples, and the problem is even more serious when the data model is extended to support order—a sine-qua-non in data stream applications. The new limitations of SQL, compounded with well-known problems in applications such as sequence queries and data mining, motivate our proposal of extending the language with user-defined aggregates (UDAs). These can be natively coded in SQL, according to simple syntactic rules that set nonblocking aggregates apart from blocking ones. We first prove that SQL with UDAs is Turing complete. We then prove that SQL with monotonic UDAs and union operators can express all monotonic set functions computable by a Turing machine ( NB -completeness) and finally extend this result to queries on sequences ordered by their timestamps. The proposed approach supports data stream models that are more sophisticated than append-only relations, along with data mining queries, and other complex applications.

The article provides the analysis of the current situation in the use of data stream management systems (DSMS) and discusses the reasons why this technology is not used to develop geographic information systems. DSMS, despite its novelty, has ceased to be a pure research project and is used in industrial applications. However, this technology is not used to design the GIS, although the necessity of processing and analyzing of spatio-temporal data streams arises in many practically important applications. The essence of the current problematic situation is the gap between new technological capabilities and the lack of a theoretical framework for the processing and analysis of spatio-temporal data streams in DSMS. Existing spatial analytics algorithms are designed for relational databases with precomputed spatial indexes and are not suitable for DSMS. The article shows that, to resolve the current problematic situation with the geoinformation systems development based on DSMS should do the following

Real-time monitoring systems are important for industry since they allow for avoiding unplanned system stops and keeping system availability high. The technical requirements for such systems include being both scalable and online, as the amount of generated data is increasing with time. Therefore, monitoring systems must integrate tools that can manage and analyze the data streams. The data stream management system is a stream processing tool that has the ability to manage and support operations on data streams in real-time. Several researchers have proposed and tested real-time monitoring systems which have the ability to search big data streams. In this paper, the research works that discuss the analysis of online data streams for fault detection in industry are reviewed. Based on the literature analysis, the industrial needs and challenges of monitoring big data streams are presented. Furthermore, feasible suggestions for improving the real-time monitoring system are proposed.

Communication-efficient processing of multiple continuous aggregate queries

There has been a rising need to handle and process streaming kind of data. It is continuous, unpredictable, time-varying in nature and could arrive in multiple rapid streams. Sensor data, web clickstreams, etc. are the examples of streaming data. One of the important issues about streaming data management systems is that it needs to be processed in real-time. That is, active rules can be defined over data streams for making the system reactive. These rules are triggered based on the events detected on the data stream, or events detected while summarizing the data or combination of both. In this paper, we study the challenges involved in monitoring events in a Data Stream Management System (DSMS) and how they differ from the same in active databases. We propose an architecture for event composition and detection in a DSMS, and then discuss an algorithm for detecting composite events defined on both the summarized data streams and the streaming data.KeywordsEvent AlgebraComposite EventEvent CompositionStreaming DataContinuous QueryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Data stream management systems usually have to process many long-running queries that are active at the same time. Multiple queries can be evaluated more efficiently together than independently, because it is often possible to share state and computation. Motivated by this observation, various Multi-Query Optimization (MQO) techniques have been proposed. However, these approaches suffer from two limitations. First, they focus on very specialized workloads. Second, integrating MQO techniques for CQL-style stream engines and those for event pattern detection engines is even harder, as the processing models of these two types of stream engines are radically different.

The continuous sliding-window query model is used widely in data stream management systems where the focus of a continuous query is limited to a set of the most recent tuples. In this paper, we show that an interesting and important class of queries over data streams cannot be answered using the sliding-window query model. Thus, we introduce a new model for continuous window queries, termed the predicate-window query model that limits the focus of a continuous query to the stream tuples that qualify a certain predicate. Predicate-window queries have some distinguishing characteristics, e.g., (1) The window predicate can be defined over any attribute in the stream tuple (ordered or unordered). (2) Stream tuples qualify and disqualify the window predicate in an out-of-order manner. In this paper, we discuss the applicability of the predicate-window query model. We will show how the existing sliding-window query models fail to answer some of the predicate-window queries. Finally, we discuss the challenges in supporting the predicate-window query model in data stream management systems.

In Continuous Data Analytics and in monitoring applications, hundreds of similar Aggregate Continuous Queries (ACQs) are registered at the Data Stream Management System (DSMS) to continuously monitor the infinite input stream of data tuples. Optimizing the processing of these ACQs is crucial in order for the DSMS to operate at the adequate required scalability. One optimization technique is to share the results of partial aggregation operations between different ACQs on the same data stream. However, finding the query execution plan that attains maximum reduction in total plan cost is computationally expensive. Weave Share, a multiple ACQs optimizer that computes query plans in a greedy fashion, was recently shown in experiments to achieve more than an order of magnitude improvement over the best existing alternatives. Maximizing the benefit of sharing, i.e., maximizing the cost-savings achieved by sharing partial aggregation results, is the goal of Weave Share. In this paper we prove that Weave Share approximates the optimal cost-savings to within a factor of 4 for a practical variant of the problem. To the best of our knowledge, this is the first theoretical guarantee provided for this problem. We also provide exact solutions for two natural special cases.

Design, realization and user evaluation of the SmartVortex Visual Query System for accessing data streams in industrial engineering applications

Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this paper, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

Recently, there is an increasing number of applications that require support for real-time processing of rapidly arriving data streams. The high volume and fast arrival rate make it infeasible to install every data item of a stream into the database. In this study, we assume transactions running over data streams execute periodically to produce new results as temporal data to be installed into the database. We call these transactions continuous transactions. They resemble continuous queries in data stream management systems plus reading non-temporal data for creating and writing temporal data. Continuous transactions are continuous, standing and persistent with pre-declared data access pattern. Conventional real-time concurrency control protocols are not designed for systems with both continuous transactions and user transactions without prior knowledge of data access pattern. We need to ensure that user transactions can read fresh data and commit before their deadlines. On the other hand, we need to issue sufficient privilege for continuous transactions to keep temporal data fresh without violating database consistency and significantly affecting the performance of user transactions. Two new time-cognizant forced wait protocols are proposed tohandle the interplay between these two types of transactions with different characteristics. These protocols are evaluated through a series of simulation experiments. We have demonstrated that taking advantage of continuous transaction and temporal data semantics in transaction scheduling can significantly improve the performance of user transactions without jeopardizing the freshness of temporal data maintained by continuous transactions.