A survey on indexing techniques for big data: taxonomy and performance evaluation

Gani, Abdullah; Siddiqa, Aisha; Shamshirband, Shahaboddin; Hanum, Fariza

doi:10.1007/s10115-015-0830-y

Cited by 210 publications

(108 citation statements)

References 83 publications

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“…Big data is characterised by a huge volume and variety of data types; created at continually increasing rates [38]. Big data analytics allows for the extraction of meaningful information from large amounts of data produced by ICTs [37][38][39].…”

Section: Research Frameworkmentioning

confidence: 99%

Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City

et al. 2017

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Abstract:As cities become increasingly complex, Information and Communication Technologies (ICTs) bring smartness into organisations and communities, contributing to a more competitive tourism destination, i.e., smart tourism destinations. Enhanced information access coupled with a new kind of tourists avid for online content and predisposed to share information on social media, allows for a better understanding of tourist behaviour regarding their spatial distribution in urban destinations. Thus, smart tourism portrays individuals as information makers, refining the available alternatives for tracking their location. Big data analytics is a technology with the potential to develop Smart City services. From the analysis of the spatial distribution of tourists in the city of Lisbon based on data collected from the 'Panoramio' social network, we identify the most popular places in the city in a context of tourist visits. This new data largely contributes to understanding the consumption of space within urban tourist destinations and therefore enables us to differentiate the overcrowded places from the ones with potential to grow. This allows decision-makers to imagine new ways of planning and managing towards a sustainable 'smart' future.

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Section: Research Frameworkmentioning

confidence: 99%

Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City

et al. 2017

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“…Non-clustered indexing has been an efficient query execution and data retrieval mechanism for medical images [22], event stream data [23] and for face databases [24]. In our previous work [25], we have elaborated that non-clustered indexes are fast in creation, robust, small in size and their computational cost is less. However, B-Tree is more feasible as these indexes are adaptable to growing size and suitable for various types of data.…”

Section: Related Workmentioning

confidence: 99%

SmallClient for big data: an indexing framework towards fast data retrieval

2016

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Numerous applications are continuously generating massive amount of data and it has become critical to extract useful information while maintaining acceptable computing performance. The objective of this work is to design an indexing framework which minimizes indexing overhead and improves query execution and data search performance with optimum aggregation of computing performance. We propose SmallClient, an indexing framework to speed up query execution. SmallClient has three modules: block creation, index creation and query execution. Block creation module supports improving data retrieval performance with minimum data uploading overhead. Index creation module allows maximum indexes on a dataset to increase index hit ratio with minimized indexing overhead. Finally, query execution module offers incoming queries to utilize these indexes. The evaluation shows that SmallClient outperforms Hadoop full scan with more than 90% search performance. Meanwhile, indexing overhead of SmallClient is reduced to approximately 50% and 80% for index size and indexing time respectively.

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“…Indexing is a significant activity even for distributed highly available big data sets to efficiently perform data retrieval operations [8]. It is impractical to apply full scan on millions of records to accomplish search of a specific result [9].…”

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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A survey on indexing techniques for big data: taxonomy and performance evaluation

Cited by 210 publications

References 83 publications

Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City

Identifying Tourist Places of Interest Based on Digital Imprints: Towards a Sustainable Smart City

SmallClient for big data: an indexing framework towards fast data retrieval

On the analysis of big data indexing execution strategies

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