Shared-Memory Parallel Hash-Based Stream Join in Continuous Data Streams

Data Stream Management Systems (DSMS) handle a particular type of database applications that involve multiple continuous data streams with inputs arriving at highly variable and unpredictable rates. Since data rate fluctuates over time in this type of applications the appropriate join tree is crucial for maintaining high system throughput. We consider the problem of finding optimal join tree for performing count based sliding window multi-joins over continuous streams. We use a unit-time based cost model to evaluate the expected performance for a given join tree. We materialize all intermediate results assuming there is enough main memory to store all partial results and window buffers. We give a polynomial time algorithm that finds the optimal join tree under our cost model for a given noncommuting (single permutation) order of streams. This algorithm can be used in conjunction with any linear order producing heuristic to give the optimal tree for that order. Our algorithm is implemented in the Jess rule engine and an extensive experimental evaluation is provided.

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.

Internet of Things (IoT) systems include many smart devices that continuously generate massive spatio-temporal data, which can be difficult to process. These continuous data streams need to be stored smartly so that query searches are efficient. In this work, we propose an efficient method, in the fog-cloud computing architecture, to index continuous and heterogeneous data streams in metric space. This method divides the fog layer into three levels: clustering, clusters processing and indexing. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to group the data from each stream into homogeneous clusters at the clustering fog level. Each cluster in the first data stream is stored in the clusters processing fog level and indexed directly in the indexing fog level in a Binary tree with Hyperplane (BH tree). The indexing of clusters in the subsequent data stream is determined by the coefficient of variation (CV) value of the union of the new cluster with the existing clusters in the cluster processing fog layer. An analysis and comparison of our experimental results with other results in the literature demonstrated the effectiveness of the CV method in reducing energy consumption during BH tree construction, as well as reducing the search time and energy consumption during a k Nearest Neighbor (kNN) parallel query search.

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.

With the emergence of large wired and wireless sensor networks, many real-time applications need to operate on continuous unbounded data streams. At the same time, many of these systems have inherent timing constraints. Providing deadline guarantees for queries over dynamic data streams is a challenging problem due to bursty data stream arrival rates and time-varying stream contents. In this paper, we propose a prediction-based quality-of-service (QoS) management scheme for periodic queries over dynamic data streams. Our QoS management scheme features novel query workload estimators, which predict the query workload using execution time profiling and input data sampling, and adjusts the query QoS levels based on online query execution time prediction. We implement our QoS management algorithm on a real-time data stream query system prototype called RTStream. Our experimental evaluation of the scheme shows that our query workload estimator performs very well even with workload fluctuations and our QoS management scheme yields better overall system utility than the existing approaches for QoS management

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.

데이터 스트림은 빠르게 연속적으로 발생하는 무제한의 데이터 튜플의 집합이다. 이러한 데이터 스트림에 대한 질의 처리 또한 연속적이고 신속해야 하며 엄격한 시공간적 제약이 요구된다. 대부분의 데이터 스트림 관리시스템(DSMS)에서는 시공간적 제약사항을 효과적으로 지키기 위해서 등록된 연속 질의들의 선택 조건(selection predicate)들을 그룹화하거나 색인처리 한다. 본 논문에서는 연속 질의들의 선택 조건들을 속성별로 그룹화한 새로운 구조체인 속성 선택체(Attribute Selection Construct)를 제안한다. 속성 선택체에는 해당 속성이 특정 질의조건에 사용되는지 여부, 부분적으로 미리 계산된 질의결과 정보, 그리고 해당 속성의 선택률 통계 등 효율적인 질의 처리를 위한 유용한 정보들이 포함된다. 또한, 대상 질의집합을 구현한 속성 선택체들 간의 처리 순서는 전체적인 질의성능에 많은 영향을 미칠 수 있기 때문에 효과적으로 속성 선택체 처리 순서를 결정할 수 있는 전략도 함께 제안된다. 마지막으로, 기존의 방법들이 포함된 다양한 실험을 통하여 제안된 방법론의 성능을 여러 각도에서 비교 검증한다. A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Query processing for such a data stream should also be continuous and rapid, which requires strict time and space constraints. In most DSMS(Data Stream Management System), the selection predicates of continuous queries are grouped or indexed to guarantee these constraints. This paper proposes a new scheme tailed an ASC(Attribute Selection Construct) that collectively evaluates selection predicates containing the same attribute in multiple continuous queries. An ASC contains valuable information, such as attribute usage status, partially pre calculated matching results and selectivity statistics for its multiple selection predicates. The processing order of those ASC's that are corresponding to the attributes of a base data stream can significantly influence the overall performance of multiple query evaluation. Consequently, a method of establishing an efficient evaluation order of multiple ASC's is also proposed. Finally, the performance of the proposed method is analyzed by a series of experiments to identify its various characteristics.

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.

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.

Streaming analysis is widely used in a variety of environments, from cloud computing infrastructures up to the network's edge. In these contexts, accurate modeling of streaming operators' performance enables fine-grained prediction of applications' behavior without the need of costly monitoring. This is of utmost importance for computationally-expensive operators like stream joins, that observe throughput and latency very sensitive to rate-varying data streams, especially when deterministic processing is required.In this paper, we present a modeling framework for estimating the throughput and the latency of stream join processing. The model is presented in an incremental step-wise manner, starting from a centralized non-deterministic stream join and expanding up to a deterministic parallel stream join. The model describes how the dynamics of throughput and latency are influenced by the number of physical input streams, as well as by the amount of parallelism in the actual processing and the requirement for determinism. We present an experimental validation of the model with respect to the actual implementation. The proposed model can provide insights that are catalytic for understanding the behavior of stream joins against different system deployments, with special emphasis on the influences of determinism and parallelization.

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

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.

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.

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.