When things matter: A survey on data-centric internet of things

Qin, Yongrui; Sheng, Quan Z.; Falkner, Nickolas; Dustdar, Schahram; Wang, Hua; Vasilakos, Athanasios V.

doi:10.1016/j.jnca.2015.12.016

Cited by 301 publications

(178 citation statements)

References 100 publications

(97 reference statements)

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“…We set the default threshold to 0.6 as this value can best balance matching speed and matching quality. We tested k in the range of [1,5]. From the results we can observe that the classification time does not change too much while the matching time has an obvious increasing trend.…”

Section: Experimental Results-parameter: Kmentioning

confidence: 99%

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Approximate Semantic Matching over Linked Data Streams

Qin

Yao

Sheng

2016

Lecture Notes in Computer Science

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Abstract. In the Internet of Things (IoT), data can be generated by all kinds of smart things. In such context, enabling machines to process and understand such data is critical. Semantic Web technologies, such as Linked Data, provide an effective and machine-understandable way to represent IoT data for further processing. It is a challenging issue to match Linked Data streams semantically based on text similarity as text similarity computation is time consuming. In this paper, we present a hashing-based approximate approach to efficiently match Linked Data streams with users' needs. We use the Resource Description Framework (RDF) to represent IoT data and adopt triple patterns as user queries to describe users' data needs. We then apply locality-sensitive hashing techniques to transform semantic data into numerical values to support efficient matching between data and user queries. We design a modified k nearest neighbors (kNN) algorithm to speedup the matching process. The experimental results show that our approach is up to five times faster than the traditional methods and can achieve high precisions and recalls.

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Section: Experimental Results-parameter: Kmentioning

confidence: 99%

“…Data that has been collected from IoT could be in various formats. IoT data could also be in large amount, which makes it difficult and costly for people to process manually [5]. This calls for the use of Semantic Web technologies to process data generated in the coming IoT era.…”

Section: Introductionmentioning

confidence: 99%

Approximate Semantic Matching over Linked Data Streams

Qin

Yao

Sheng

2016

Lecture Notes in Computer Science

Self Cite

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“…Therefore, big data introduces new challenges for security [14,23], processing and analytics such as quick and up-to-date responses of a search query and in-time availability of data [24]. For instance, data obtained from sensor networks like urban management, environment and industrial installation [17] introduces storing, cleansing, query execution and other challenges like security, visualization and analytics [15,22]. Similarly in the field of body sensor networks, increasing costs of healthcare and ageing of population are major subjects for researchers which have critical information retrieval requirements.…”

Section: Introductionmentioning

confidence: 99%

On the analysis of big data indexing execution strategies

Siddiqa

Karim

Saba

et al. 2017

IFS

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Abstract. Efficient response to search queries is very crucial for data analysts to obtain timely results from big data spanned over heterogeneous machines. Currently, a number of big-data processing frameworks are available in which search operations are performed in distributed and parallel manner. However, implementation of indexing mechanism results in noticeable reduction of overall query processing time. There is an urge to assess the feasibility and impact of indexing towards query execution performance. This paper investigates the performance of state-of-the-art clustered indexing approaches over Hadoop framework which is de facto standard for big data processing. Moreover, this study leverages a comparative analysis of nonclustered indexing overhead in terms of time and space taken by indexing process for varying volume data sets with increasing Index Hit Ratio. Furthermore, the experiments evaluate performance of search operations in terms of data access and retrieval time for queries that use indexes. We then validated the obtained results using Petri net mathematical modeling. We used multiple data sets in our experiments to manifest the impact of growing volume of data on indexing and data search and retrieval performance. The results and highlighted challenges favorably lead researchers towards improved implication of indexing mechanism in perspective of data retrieval from big data. Additionally, this study advocates selection of a non-clustered indexing solution so that optimized search performance over big data is obtained.

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“…The general aim of ambient systems is to provide an environment wherein computing devices will become invisibly embedded into the environment and interact with their environment in a seamless, trustworthy, and natural manner [2]. Ambient intelligence system research builds upon advances in sensors and sensor networks, the Internet of Things (IoT), pervasive computing, and artificial intelligence and have experienced tremendous growth in the last few years [3,4].…”

Section: Introductionmentioning

confidence: 99%