What, where, and when

Nepomnyachiy, Sergey; Gelley, Bluma; Jiang, Wei; Minkus, Tehila

doi:10.1145/2675354.2675358

Cited by 16 publications

(2 citation statements)

References 37 publications

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“…References [20], [21], [22], and [23] have worked on keywords based indexing. References [20] and [21] was comprised of Name and description-based indexing.…”

Section: ) Indexingmentioning

confidence: 99%

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Indexing in WoT to Locate Indoor Things

et al. 2023

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The Web of Things (WoT) is an enhanced form of the Internet of Things (IoT) that has changed the trend of life nowadays. Due to IoT, life is transformed into smart life, such as smart buildings, smart vehicles, smart agriculture, smart businesses, etc., by connecting a certain number of things to the internet. Many people are now working on ways to locate indoor things to interact and exchange data between smart things and web services and apps, which is called ''WoT,'' or ''Web of Things.'' To interact and exchange the data, researchers need a search engine on WoT. However, locating indoor things in the Web of Things (WoT) remains a challenge due to the lack of a unified indexing system. In this research, we propose a novel approach to index indoor things in the WoT by leveraging machine learning and web technologies. Our approach includes a data preprocessing step, where we extract relevant features from the sensor data, followed by a clustering algorithm to group similar devices. We then use a semantic model to assign meaning to the clusters and develop a search engine to enable efficient searching of indoor things. Our proposed approach improves the accuracy and efficiency of locating indoor things in the WoT, paving the way for new applications in smart homes, healthcare, and industrial automation.

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“…References [20], [21], [22], and [23] have worked on keywords based indexing. References [20] and [21] was comprised of Name and description-based indexing.…”

Section: ) Indexingmentioning

confidence: 99%

“…References [20] and [21] was comprised of Name and description-based indexing. Similarly, [22] and [23] presented a search engine with only description-based indexing. Meanwhile, [24] proposed an indexing approach that performs indexing on behalf of Services.…”

Section: ) Indexingmentioning

confidence: 99%

Indexing in WoT to Locate Indoor Things

et al. 2023

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Location- and keyword-based querying of geo-textual data: a survey

2021

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With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and

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Hierarchical information quadtree: efficient spatial temporal image search for multimedia stream

Zhang

Chen

Zhu

et al. 2018

Multimed Tools Appl

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Massive amount of multimedia data that contain times-tamps and geographical information are being generated at an unprecedented scale in many emerging applications such as photo sharing web site and social networks applications. Due to their importance, a large body of work has focused on efficiently computing various spatial image queries. In this paper,we study the spatial temporal image query which considers three important constraints during the search including time recency, spatial proximity and visual relevance. A novel index structure, namely Hierarchical Information Quadtree(HI-Quadtree), to efficiently insert/delete spatial temporal images with high arrive rates. Base on HI-Quadtree an efficient algorithm is developed to support spatial temporal image query. We show via extensive experimentation with real spatial databases clearly demonstrate the efficiency of our methods.

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What, where, and when

Cited by 16 publications

References 37 publications

Indexing in WoT to Locate Indoor Things

Indexing in WoT to Locate Indoor Things

Location- and keyword-based querying of geo-textual data: a survey

Hierarchical information quadtree: efficient spatial temporal image search for multimedia stream

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