MODELING CONTINUOUS QUERIES OVER DATA STREAMS

2009 ◽  
pp. 49-93
Author(s):  
Sharma Chakravarthy ◽  
Qingchun Jiang
Keyword(s):  
Data Streams ◽  
Continuous Queries

Continuous Probabilistic Reverse Skyline Monitoring over Uncertain Data Streams

2013 ◽  
Vol 380-384 ◽  
pp. 2681-2686
Author(s):  
Yong Tao Yang ◽  
Yi Jie Wang ◽  
Min Guo ◽  
Xiao Yong Li
Keyword(s):  
Data Streams ◽  
Uncertain Data ◽  
Continuous Queries ◽  
Skyline Queries ◽  
Marketing Decision ◽  
Formal Definitions ◽  
Uncertain Data Streams ◽  
Data Elements ◽  
Pruning Techniques ◽  
Reverse Skyline

Reverse skyline is useful for supporting many applications, such as marketing decision,environmental monitoring. Since the uncertainty of data is inherent in many scenarios, there is a needfor processing probabilistic reverse skyline queries. In this paper, we study the problem of efficientlyprocessing these queries on uncertain data streams. We first show the formal definitions of reverseskyline probability and probabilistic reverse skyline. Then we propose a new algorithm called CPRSto maintain the most recent N uncertain data elements and to process continuous queries on them.CPRS is based on R-tree, and efficient pruning techniques, one of which is based on a new structurenamed Characteristic Rectangle, are incorporated into it to handling the extra computing complexityarising from the uncertainty of data. Finally, extensive experiments demonstrate that our techniquesare very efficient in handling uncertain data streams.

scholarly journals Support for Adaptive and Distributed Deployment of CEP Continuous Queries for the IoMT

2020 ◽  
Author(s):  
Fernando Benedito Veras Magalhães ◽  
Francisco José da Silva e Silva ◽  
Markus Endler
Keyword(s):  
Real World ◽  
Data Streams ◽  
High Volume ◽  
Complex Event Processing ◽  
Continuous Queries ◽  
Event Processing ◽  
The Real ◽  
Volume Velocity ◽  
Controlling Elements ◽  
Distributed Deployment

The current dissemination of IoT increases the deployment of stream processing solutions for monitoring and controlling elements of the real-world. One of those solutions is Complex Event Processing (CEP), and to handle the high volume, velocity and volatility of data streams from IoT sensors the CEP pipeline should be distributed, preferably having CEP operators both in the cloud/cluster and in edge devices. In this paper, we present a model for a distributed CEP platform and an implementation of this model called Global CEP Manager (GCM). GCM is a service of the ContextNet middleware that supports the deployment and dynamic rearrangement of CEP queries to CEP engines executing in the cloud and in M-Hubs, that are ContextNet’s mobile edge devices.

scholarly journals Preprocessing Method for Handling Multi-Way Join Continuous Queries over Data Streams

2012 ◽  
Vol 13 (3) ◽  
pp. 93-105
Author(s):  
Ki-Yeon Seo ◽  
Joo-Il Lee ◽  
Won-Suk Lee
Keyword(s):  
Data Streams ◽  
Continuous Queries ◽  
Preprocessing Method

scholarly journals Continuous queries over data streams

2001 ◽  
Vol 30 (3) ◽  
pp. 109-120 ◽  
Author(s):  
Shivnath Babu ◽  
Jennifer Widom
Keyword(s):  
Data Streams ◽  
Continuous Queries

Processing Data Streams

2020 ◽  
pp. 20-39
Author(s):  
Parimala N.
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Continuous Queries ◽  
Stream Data ◽  
Continuous Sequence ◽  
Static Data ◽  
Transactional Data ◽  
Events Data

A data stream is a real-time continuous sequence that may be comprised of data or events. Data stream processing is different from static data processing which resides in a database. The data stream data is seen only once. It is too voluminous to store statically. A small portion of data called a window is considered at a time for querying, computing aggregates, etc. In this chapter, the authors explain the different types of window movement over incoming data. A query on a stream is repeatedly executed on the new data created by the movement of the window. SQL extensions to handle continuous queries is addressed in this chapter. Streams that contain transactional data as well as those that contain events are considered.

Data Streams as an Element of Modern Decision Support

2011 ◽  
pp. 941-949
Author(s):  
Damianos Chatziantoniou ◽  
George Doukidis
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Data Management ◽  
Data Streams ◽  
Database Management System ◽  
Continuous Queries ◽  
Multiple Sources ◽  
Data Intensive ◽  
Executive Manager ◽  
Future Data

Traditional decision support systems (DSS) and executive information systems (EIS) gather and present information from several sources for business purposes. It is an information technology to help the knowledge worker (executive, manager, analyst) make faster and better decisions. So far, these data were stored statically and persistently in a database, typically in a data warehouse. Data warehouses collect masses of operational data, allowing analysts to extract information by issuing decision support queries on the otherwise discarded data. In a typical scenario, an organization stores a detailed record of its operations in a database, which is then analyzed to improve efficiency, detect sales opportunities, and so on. Performing complex analysis on these data is an essential component of these organizations’ businesses. Chaudhuri and Dayal (1997) present an excellent survey on decision-making and online analytical processing (OLAP) technologies for traditional database systems. ?n many applications however, it may not be possible to process queries within a database management system (DBMS). These applications involve data items that arrive online from multiple sources in a continuous, rapid and time-varying fashion (Babcock et. al., 2002). These data may or may not be stored in a database. As a result, a new class of data-intensive applications has recently attracted a lot of attention: applications in which the data is modeled not as persistent relations but rather as transient data streams. Examples include financial applications (streams of transactions or ticks), network monitoring (stream of packets), security, telecommunication data management (stream of calls or call packets), web applications (clickstreams), manufacturing, wireless sensor networks (measurements), RFID data, and others. In data streams we usually have “continuous” queries (Terry et. al., 1992; Babu & Widom, 2002) rather than “one-time.” The answer to a continuous query is produced over time, reflecting the stream data seen so far. Answers may be stored and updated as new data arrives or may be produced as data streams themselves. Continuous queries can be used for monitoring, alerting, security, personalization, etc. Data streams can be either transactional (i.e., log interactions between entities, such as credit card purchases, web clickstreams, phone calls), or measurement (i.e., monitor evolution of entity states, such as physical phenomena, road traffic, temperature, network). How to best model, express and evaluate complex queries over data streams is an open and difficult problem. This involves data modeling, rich querying capabilities to support real-time decision support and mining, and novel evaluation and optimization processing techniques. In addition, the kind of decision support over data streams is quite different from “traditional” decision-making: decisions are “tactical” rather than “strategic.” Research on data streams is currently among the most active areas in database research community. Flexible and efficient stream querying will be a crucial component of any future data management and decision support system (Abiteboul et al., 2005).

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