Inverted index maintenance strategy for flashSSDs: Revitalization of in-place index update strategy

Jung, Wonmook; Roh, Hyun Seog; Shin, Mincheol; Park, Sang‐Hyun

doi:10.1016/j.is.2014.11.004

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…It is an effective method in which there is a posting list for each indexed term, and each posting list consists of a document identifier, the number of times the term occurs which means an in-document frequency, and positions where the term appears in the document, i.e. offsets [10].…”

Section: Inverted Indexmentioning

confidence: 99%

Feature Pair Index Graph for Clustering

Karthika

Janet

2019

Journal of Intelligent Systems

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Abstract Text documents are significant arrangements of various words, while images are significant arrangements of various pixels/features. In addition, text and image data share a similar semantic structural pattern. With reference to this research, the feature pair is defined as a pair of adjacent image features. The innovative feature pair index graph (FPIG) is constructed from the unique feature pair selected, which is constructed using an inverted index structure. The constructed FPIG is helpful in clustering, classifying and retrieving the image data. The proposed FPIG method is validated against the traditional KMeans++, KMeans and Farthest First cluster methods which have the serious drawback of initial centroid selection and local optima. The FPIG method is analyzed using Iris flower image data, and the analysis yields 88% better results than Farthest First and 28.97% better results than conventional KMeans in terms of sum of squared errors. The paper also discusses the scope for further research in the proposed methodology.

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Section: Inverted Indexmentioning

confidence: 99%

Feature Pair Index Graph for Clustering

Karthika

Janet

2019

Journal of Intelligent Systems

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“…Inverted index is regarded as a core data structure of information retrieval systems, especially in search engines [31]. Inverted index models can enhance the efficiency of the service discovery process by offering better response times than those of the sequential matchmaking processes, and by providing better recall rates than those approaches without an inverted index.…”

Section: Indexing Modelmentioning

confidence: 99%

An Efficient Indexing Model for the Fog Layer of Industrial Internet of Things

Miao

Liu

et al. 2018

IEEE Trans. Ind. Inf.

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Given the recent proliferation in the number of smart devices connected to the Internet, the era of Internet of Things (IoT) is challenged with massive amounts of data generation. Fog Computing is gaining popularity and is being increasingly deployed in various latency-sensitive application domains including industrial IoTs. However, efficient discovery of services is one of the prevailing issues in the fog nodes of industrial IoTs which restrain their efficiencies in availing appropriate services to the clients. To address this issue, this paper proposes a novel efficient multilevel index model based on equivalence relation, named the DM-index model, for service maintenance and retrieval in the fog layer of industrial IoTs to eliminate redundancy, narrow the search space, reduce both the traversed number of services and retrieval time, ultimately to improve the service discovery efficiency. The efficiency of the proposed index model has been verified theoretically and evaluated experimentally, which demonstrates that the proposed model is effective in achieving much better service discovery and retrieval performance than the sequential and inverted index models.

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