IoT‐SVKSearch: a real‐time multimodal search engine mechanism for the internet of things

Ding, Zhiming; Chen, Zhikui; Yang, Qi

doi:10.1002/dac.2647

Cited by 37 publications

(29 citation statements)

References 32 publications

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“…At present, social media data have played an important role in online public opinion, natural disaster monitoring, and environmental law enforcement. (5) Internet of Things data (Alelaiwi, 2017;Ding, Chen, & Yang, 2014): all kinds of sensor monitoring data, including environmental protection, meteorology, water, pipelines monitoring, and wearable devices, intelligent household and others. Compared with the traditional internet, the Internet of Things data are generated with higher frequency and greater variety.…”

Section: Big Spatial Datamentioning

confidence: 99%

Big spatial vector data management: a review

Yao

2018

Big Earth Data

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Spatial vector data with high-precision and wide-coverage has exploded globally, such as land cover, social media, and other datasets, which provides a good opportunity to enhance the national macroscopic decision-making, social supervision, public services, and emergency capabilities. Simultaneously, it also brings great challenges in management technology for big spatial vector data (BSVD). In recent years, a large number of new concepts, parallel algorithms, processing tools, platforms, and applications have been proposed and developed to improve the value of BSVD from both academia and industry. To better understand BSVD and take advantage of its value effectively, this paper presents a review that surveys recent studies and research work in the data management field for BSVD. In this paper, we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management. It aims to help interested readers to learn about the latest research advances and choose the most suitable big data technologies and approaches depending on their system architectures. To support them more fully, firstly, we identify new concepts and ideas from numerous scholars about geographic information system to focus on BSVD scope in the big data era. Then, we conclude systematically not only the most recent published literatures but also a global view of main spatial technologies of BSVD, including data storage and organization, spatial index, processing methods, and spatial analysis. Finally, based on the above commentary and related work, several opportunities and challenges are listed as the future research interests and directions for reference.

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Section: Big Spatial Datamentioning

confidence: 99%

Big spatial vector data management: a review

Yao

2018

Big Earth Data

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“…These kind of textual descriptions can be crawled and indexed to build simple, text-based search systems. For example, Global Sensor Networks (GSN) [ 25 ], SenseWeb [ 29 ], OSIRIS [ 38 ], Dyser [ 30 ], Microsearch [ 31 ], Snoogle [ 32 ], and IoT-SVK [ 37 ] (which is a hybrid real-time search engine framework for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions), all apply text based indexing and searching. Techniques for text indexing can also be used for indexing values, e.g., LiveWeb [ 33 ] and IoT-SVK [ 37 ] apply such indexing techniques for sensor values and provide search functionalities over the given values.…”

Section: Classification Viewpoint—basic Principlesmentioning

confidence: 99%

“…WoT objects and data have a strong focus on locality; this implies that spatial information is vital for description and search. In some cases, spatial information and other features of objects and data are separated to provide search services [ 23 , 37 , 38 , 39 , 40 , 41 , 42 ]. Spatial information is typically represented by latitude and longitude coordinates (sometimes including altitude as well).…”

Section: Classification Viewpoint—basic Principlesmentioning

confidence: 99%

Search Techniques for the Web of Things: A Taxonomy and Survey

Zhou

Wang

et al. 2016

Sensors

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The Web of Things aims to make physical world objects and their data accessible through standard Web technologies to enable intelligent applications and sophisticated data analytics. Due to the amount and heterogeneity of the data, it is challenging to perform data analysis directly; especially when the data is captured from a large number of distributed sources. However, the size and scope of the data can be reduced and narrowed down with search techniques, so that only the most relevant and useful data items are selected according to the application requirements. Search is fundamental to the Web of Things while challenging by nature in this context, e.g., mobility of the objects, opportunistic presence and sensing, continuous data streams with changing spatial and temporal properties, efficient indexing for historical and real time data. The research community has developed numerous techniques and methods to tackle these problems as reported by a large body of literature in the last few years. A comprehensive investigation of the current and past studies is necessary to gain a clear view of the research landscape and to identify promising future directions. This survey reviews the state-of-the-art search methods for the Web of Things, which are classified according to three different viewpoints: basic principles, data/knowledge representation, and contents being searched. Experiences and lessons learned from the existing work and some EU research projects related to Web of Things are discussed, and an outlook to the future research is presented.

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“…The fourth paper entitled 'IoT-SVKSearch: a real-time multimodal search engine mechanism for the internet of things' by Zhiming Ding et al proposes a real-time multimodal search engine framework, IoT-SVKSearch, for retrieving massive and heterogeneous Internet of Things (IoT) sampling data [8]. Through the distributed global indices built on the extracted full-text keywords, the spatial-temporal attributes, and the sampling values, IoT-SVKSearch can support multi-modal search conditions including keyword-based, spatial-temporal, and value-based constraints.…”

mentioning

confidence: 99%

Advanced communication systems for enhanced big data technology and applications

Jeong

Yang

et al. 2014

Int J Communication

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EditorialAdvanced communication systems for enhanced big data technology and applications INTRODUCTIONThere has been a tremendous increase in both the capacity and complexity of data in the last few years, and for this reason, big data technology has been one of the most conspicuously emerging research areas. In big data technology, communication systems are implemented in such ways that data are collected from heterogeneous services and integrated seamlessly. To make big data technology more user-centric, further exploration is called for in the areas of data utilization and communication system in heterogeneous network environments [1][2][3][4]. Therefore, many scientists have been actively engaged in researching in the following fields in connection with big data technology: networking, data mining, information security, and privacy protection. This special issue aims to focus on the topics of communication system in enhanced big data technology and application [1][2][3][4].Areas of interest for the special issue 'Advanced Communication Systems for Enhanced Big Data Technology and Applications' include the following topics: big data processing and analytics in communication systems; modeling, semantics, and protocols for big data; agent technology and context-aware system; big data mining for network optimization; social networks influence on communication systems; real-time and streaming data processing and analytics; network protocol for ubiquitous data gathering; data management for mobile and pervasive computing and networking; architecture and optimization of advanced big data technology; cloud/grid/stream computing for big data; applications and security for big data; testbeds and case studies for big data technologies and applications.We have received many manuscripts. Only six manuscripts with high quality were finally selected for this special issue. Each manuscript selected was blindly reviewed by at least three reviewers consisting of guest editors and external reviewers. We present a brief overview of each manuscript in the following. RELATED WORKSThe first paper entitled 'MR-tree: an efficient index for MapReduce' by Chunsheng Li et al. proposes a novel method, called MR-tree, to index multiple dimensional data in MapReduce framework [5]. The generation of an MR-tree contains two phases. In the first phase, it creates the leaflevel index layer. In the second phase, it uses an iterative manner to build internal-level layers. The main contribution of this paper was MR-tree, a tree-like structure to index multiple dimensional data. The construction of an MR-tree contains two phases, namely, leaf-level building phase and internal-level building phase. All entries in leaf layer are constructed in the first phase, whereas the rest of the entries are constructed in the second phase. Experimental results indicate that the proposed method can run efficiently.The second paper entitled 'Two-phase grouping-based resource management for big data processing in mobile cloud computing' by JiSu Park et al. propos...

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IoT‐SVKSearch: a real‐time multimodal search engine mechanism for the internet of things

Cited by 37 publications

References 32 publications

Big spatial vector data management: a review

Big spatial vector data management: a review

Search Techniques for the Web of Things: A Taxonomy and Survey

Advanced communication systems for enhanced big data technology and applications

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