XML Stream Query Processing

Stream applications bring the challenge of efficiently processing queries on sequentially accessible XML data streams. In this chapter, the authors study the current techniques and open challenges of XML stream processing. Firstly, they examine the input data semantics in XML streams and introduce the state-of-the-art of XML stream processing. Secondly, they compare and contrast the automatonbased and algebra-based techniques used in XML stream query execution. Thirdly, they study different optimization strategies that have been investigated for XML stream processing – in particular, they discuss cost-based optimization as well as schema-based optimization strategies. Lastly but not least, the authors list several key open challenges in XML stream processing.

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XML Stream Processing

Advanced Applications and Structures in XML Processing ◽

10.4018/978-1-61520-727-5.ch009 ◽

2010 ◽

pp. 184-206

Author(s):

Junichi Tatemura

Keyword(s):

Query Processing ◽

Time Complexity ◽

Stream Processing ◽

Query Evaluation ◽

Streaming Algorithms ◽

Buffer Memory ◽

Recent Advances ◽

Xml Stream ◽

Multiple Query ◽

Partial Query

This chapter reviews recent advances on stream XML query evaluation algorithms with stack-based encoding of intermediary data. Originally proposed for disk-resident XML, the stack-based architecture has been extended for streaming algorithms for both single and multiple query processing, ranging from XPath filtering to more complex XQuery. The key benefit of the stack-based architecture is its succinct encoding of partial query results, which can cause exponential enumeration if encoded naively. In addition, the chapter discusses opportunities to integrate benefits demonstrated in the reviewed work. For single-query processing, a sketch is given for an integrated algorithm, StreamTwig2Stack, that achieves all the benefits of existing algorithms in terms of functionality, time complexity, and buffer memory optimality.

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A Survey of IoT Stream Query Execution Latency Optimization within Edge and Cloud

Wireless Communications and Mobile Computing ◽

10.1155/2021/4811018 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Fatima Abdullah ◽

Limei Peng ◽

Byungchul Tak

Keyword(s):

Query Processing ◽

Streaming Data ◽

Query Execution ◽

Cloud Environment ◽

Future Directions ◽

Stream Query Processing ◽

Execution Model ◽

Iot Devices ◽

Edge Based ◽

Execution Models

IoT (Internet of Things) streaming data has increased dramatically over the recent years and continues to grow rapidly due to the exponential growth of connected IoT devices. For many IoT applications, fast stream query processing is crucial for correct operations. To achieve better query performance and quality, researchers and practitioners have developed various types of query execution models—purely cloud-based, geo-distributed, edge-based, and edge-cloud-based models. Each execution model presents unique challenges and limitations of query processing optimizations. In this work, we provide a comprehensive review and analysis of query execution models within the context of the query execution latency optimization. We also present a detailed overview of various query execution styles regarding different query execution models and highlight their contributions. Finally, the paper concludes by proposing promising future directions towards advancing the query executions in the edge and cloud environment.

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Multi-dimensional phenomenon-aware stream query processing

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '09 ◽

10.1145/1653771.1653848 ◽

2009 ◽

Author(s):

Ashish Bindra ◽

Ankur Teredesai ◽

Mohamed H. Ali ◽

Walid G. Aref

Keyword(s):

Query Processing ◽

Stream Query Processing

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XML Stream Processing

Encyclopedia of Database Systems ◽

10.1007/978-1-4614-8265-9_473 ◽

2018 ◽

pp. 4803-4807

Author(s):

Christoph Koch

Keyword(s):

Stream Processing ◽

Xml Stream

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A Containerized Approach for Allocating Distributed Stream Queries to Fog Nodes

10.36227/techrxiv.14151650.v1 ◽

2021 ◽

Author(s):

Hamed Hasibi ◽

Saeed Sedighian Kashi

Keyword(s):

Fog Computing ◽

Stream Processing ◽

Stream Data ◽

Process Data ◽

Stream Query Processing ◽

Tremendous Amount ◽

Stream Processing Engines ◽

Iot Devices ◽

Distributed Stream Processing

Fog computing brings cloud capabilities closer to the Internet of Things (IoT) devices. IoT devices generate a tremendous amount of stream data towards the cloud via hierarchical fog nodes. To process data streams, many Stream Processing Engines (SPEs) have been developed. Without the fog layer, the stream query processing executes on the cloud, which forwards much traffic toward the cloud. When a hierarchical fog layer is available, a complex query can be divided into simple queries to run on fog nodes by using distributed stream processing. In this paper, we propose an approach to assign stream queries to fog nodes using container technology. We name this approach Stream Queries Placement in Fog (SQPF). Our goal is to minimize end-to-end delay to achieve a better quality of service. At first, in the emulation step, we make docker container instances from SPEs and evaluate their processing delay and throughput under different resource configurations and queries with varying input rates. Then in the placement step, we assign queries among fog nodes by using a genetic algorithm. The practical approach used in SQPF achieves a near-the-best assignment based on the lowest application deadline in real scenarios, and evaluation results are evidence of this goal.

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Foundation of XML Stream Processing Techniques

Advances in Data Mining and Database Management - Design, Performance, and Analysis of Innovative Information Retrieval ◽

10.4018/978-1-4666-1975-3.ch004 ◽

2013 ◽

pp. 48-59

Author(s):

Weidong Yang ◽

Hao Zhu

Keyword(s):

Optimization Technique ◽

Stream Processing ◽

Research Community ◽

Processing Methods ◽

Xml Data ◽

Research Status ◽

Basic Concepts ◽

Xml Stream ◽

Processing Techniques ◽

Area Section

The problem of processing streaming XML data is gaining widespread attention from the research community, and various XML stream processing methods are put forward, including automaton-based methods, index-based methods, and so forth. In this chapter, the basic concepts and several existing typical approaches of XML stream processing are discussed. Section 1 introduces the background and current research status of this area. Section 2 focuses on the discussion of automaton-based methods, for example, X/YFilter, XPush, et cetera. In section 3, the index-based methods are given. In section 4, other methods such us Fist and XTrie are discussed briefly. Section 4 discusses some optimization technique of XML stream processing. Section 5 summarizes this chapter.

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