Towards Efficient Retrieval of Top-k Entities in Systems of Engagement

Mondal, Anirban; Padhariya, Nilesh; Mohania, Mukesh

doi:10.1007/978-3-030-62008-0_4

Cited by 1 publication

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, a preliminary version of our paper has appeared in [4]. In this paper, we extend our work in [4] in the following ways.…”

Section: Our Contributionsmentioning

confidence: 87%

“…Notably, a preliminary version of our paper has appeared in [4]. In this paper, we extend our work in [4] in the following ways. First, in contrast with our work in [4], in this work, we additionally consider the case of geo-tagged entities.…”

Section: Our Contributionsmentioning

confidence: 87%

“…In this paper, we extend our work in [4] in the following ways. First, in contrast with our work in [4], in this work, we additionally consider the case of geo-tagged entities. Moreover, we use an R-tree-based approach for efficiently determining the top-k entities in an SOE for a given queried spatial window (region).…”

Section: Our Contributionsmentioning

confidence: 96%

See 2 more Smart Citations

Efficient Indexing of Top-k Entities in Systems of Engagement with Extensions for Geo-tagged Entities

et al. 2021

Self Cite

View full text Add to dashboard Cite

Next-generation enterprise management systems are beginning to be developed based on the Systems of Engagement (SOE) model. We visualize an SOE as a set of entities. Each entity is modeled by a single parent document with dynamic embedded links (i.e., child documents) that contain multi-modal information about the entity from various networks. Since entities in an SOE are generally queried using keywords, our goal is to efficiently retrieve the top-k entities related to a given keyword-based query by considering the relevance scores of both their parent and child documents. Furthermore, we extend the afore-mentioned problem to incorporate the case where the entities are geo-tagged. The main contributions of this work are three-fold. First, it proposes an efficient bitmap-based approach for quickly identifying the candidate set of entities, whose parent documents contain all queried keywords. A variant of this approach is also proposed to reduce memory consumption by exploiting skews in keyword popularity. Second, it proposes the two-tier HI-tree index, which uses both hashing and inverted indexes, for efficient document relevance score lookups. Third, it proposes an R-tree-based approach to extend the afore-mentioned approaches for the case where the entities are geo-tagged. Fourth, it performs comprehensive experiments with both real and synthetic datasets to demonstrate that our proposed schemes are indeed effective in providing good top-k result recall performance within acceptable query response times.

show abstract

“…Notably, a preliminary version of our paper has appeared in [4]. In this paper, we extend our work in [4] in the following ways.…”

Section: Our Contributionsmentioning

confidence: 87%