Name
Title | ||
|---|---|---|
Efficient Indexing of Top-k Entities in Systems of Engagement with Extensions for Geo-tagged Entities |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s41019-021-00173-1 | DATA SCIENCE AND ENGINEERING |
Keywords | DocType | Volume |
Indexing, Top-k entity retrieval, Systems of engagement, Geo-tagged entities, R-tree | Journal | 6 |
Issue | ISSN | Citations |
4 | 2364-1185 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anirban Mondal | 1 | 386 | 31.29 |
| Ayaan Kakkar | 2 | 0 | 0.34 |
| Nilesh Padhariya | 3 | 21 | 3.37 |
| Mukesh Mohania | 4 | 496 | 42.79 |