scholarly journals Hypothetical Answers to Continuous Queries over Data Streams

2020 ◽  
Vol 34 (03) ◽  
pp. 2798-2805
Author(s):  
Luís Cruz-Filipe ◽  
Isabel Nunes ◽  
Graça Gaspar
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Online Algorithm ◽  
Continuous Queries ◽  
Current Information ◽  
Available Information

Continuous queries over data streams often delay answers until some relevant input arrives through the data stream. These delays may turn answers, when they arrive, obsolete to users who sometimes have to make decisions with no help whatsoever. Therefore, it can be useful to provide hypothetical answers – “given the current information, it is possible that X will become true at time t” – instead of no information at all. In this paper we present a semantics for queries and corresponding answers that covers such hypothetical answers, together with an online algorithm for updating the set of facts that are consistent with the currently available information.

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.

scholarly journals Analysis of Data Stream Processing At Edge Layer for Internet of Things

2020 ◽  
Vol 2 (1) ◽  
pp. 26-37
Author(s):  
Dr. Pasumponpandian
Keyword(s):  
Internet Of Things ◽  
Data Streams ◽  
Data Stream ◽  
Smart Cities ◽  
Stream Processing ◽  
Middle Layer ◽  
Cloud Services ◽  
Decentralized Systems ◽  
Data Stream Processing ◽  
Edge Layer

The progress of internet of things at a rapid pace and simultaneous development of the technologies and the processing capabilities has paved way for the development of decentralized systems that are relying on cloud services. Though the decentralized systems are founded on cloud complexities still prevail in transferring all the information’s that are been sensed through the IOT devices to the cloud. This because of the huge streams of information’s gathered by certain applications and the expectation to have a timely response, incurring minimized delay, computing energy and enhanced reliability. So this kind of decentralization has led to the development of middle layer between the cloud and the IOT, and was termed as the Edge layer, meaning bringing down the service of the cloud to the user edge. The paper puts forth the analysis of the data stream processing in the edge layer taking in the complexities involved in the computing the data streams of IOT in an edge layer and puts forth the real time analytics in the edge layer to examine the data streams of the internet of things offering a data- driven insight for parking system in the smart cities.

Dynamic Plan Migration for Snapshot-Equivalent Continuous Queries in Data Stream Systems

2006 ◽  
pp. 497-516 ◽  
Author(s):  
Jürgen Krämer ◽  
Yin Yang ◽  
Michael Cammert ◽  
Bernhard Seeger ◽  
Dimitris Papadias
Keyword(s):  
Data Stream ◽  
Continuous Queries ◽  
Stream Systems

Knowledge Discovery From Evolving Data Streams

2019 ◽  
pp. 19-39
Author(s):  
Prasanna Lakshmi Kompalli
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Data Stream Mining ◽  
Time Data ◽  
Stream Mining ◽  
New Challenges ◽  
Mining Data Streams ◽  
Different Sources

Data coming from different sources is referred to as data streams. Data stream mining is an online learning technique where each data point must be processed as the data arrives and discarded as the processing is completed. Progress of technologies has resulted in the monitoring these data streams in real time. Data streams has created many new challenges to the researchers in real time. The main features of this type of data are they are fast flowing, large amounts of data which are continuous and growing in nature, and characteristics of data might change in course of time which is termed as concept drift. This chapter addresses the problems in mining data streams with concept drift. Due to which, isolating the correct literature would be a grueling task for researchers and practitioners. This chapter tries to provide a solution as it would be an amalgamation of all techniques used for data stream mining with concept drift.

Exploring Calendar-Based Pattern Mining in Data Streams

2010 ◽  
pp. 342-360
Author(s):  
Rodrigo Salvador Monteiro ◽  
Geraldo Zimbrão ◽  
Holger Schwarz ◽  
Bernhard Mitschang ◽  
Jano Moreira de Souza
Keyword(s):  
Data Warehouse ◽  
Data Streams ◽  
Data Stream ◽  
Pattern Mining ◽  
A Priori ◽  
Frequent Itemsets ◽  
Detailed Data ◽  
Series Of Experiments ◽  
Working Day

Calendar-based pattern mining aims at identifying patterns on specific calendar partitions. Potential calendar partitions are for example: every Monday, every first working day of each month, every holiday. Providing flexible mining capabilities for calendar-based partitions is especially challenging in a data stream scenario. The calendar partitions of interest are not known a priori and at each point in time only a subset of the detailed data is available. The authors show how a data warehouse approach can be applied to this problem. The data warehouse that keeps track of frequent itemsets holding on different partitions of the original stream has low storage requirements. Nevertheless, it allows to derive sets of patterns that are complete and precise. Furthermore, the authors demonstrate the effectiveness of their approach by a series of experiments.

Research on Sequence Query Processing Techniques over Data Streams

2013 ◽  
Vol 284-287 ◽  
pp. 3507-3511 ◽  
Author(s):  
Edgar Chia Han Lin
Keyword(s):  
Query Processing ◽  
Data Streams ◽  
Management System ◽  
Data Stream ◽  
Sensor Data ◽  
Data Management System ◽  
Content Filtering ◽  
Data Stream Management ◽  
Stream Management ◽  
Great Progress

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 project will study the issues related to two types of data. First, we will focus on the content filtering on single-attribute streams, such as sensor data. Second, 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.

An Efficient Frequent Patterns Mining Algorithm over Data Streams Based on FPD-Graph

2012 ◽  
Vol 433-440 ◽  
pp. 4457-4462 ◽  
Author(s):  
Jun Shan Tan ◽  
Zhu Fang Kuang ◽  
Guo Gui Yang
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Data Generation ◽  
Experiment Data ◽  
Mining Algorithm ◽  
Head Node

The design of synopses structure is an important issue of frequent patterns mining over data stream. A data stream synopses structure FPD-Graph which is based on directed graph is proposed in this paper. The FPD-Graph contains list head node FPDG-Head and list node FPDG-Node. The operations of FPD-Graph consist of insert operation and deletion operation. A frequent pattern mining algorithm DGFPM based on sliding window over data stream is proposed in this paper. The IBM synthesizes data generation which output customers shopping a data are adopted as experiment data. The DGFPM algorithm not only has high precision for mining frequent patterns, but also has low processing time.

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