Name
Title | ||
|---|---|---|
A Data Indexing Technique to Improve the Search Latency of AND Queries for Large Scale Textual Documents |
Abstract | ||
|---|---|---|
Boolean AND queries (BAQ) are one of the most important types of queries used in text searching. In this paper, a graph-based indexing technique is proposed to improve the search latency of BAQ. It shows how a graph structure represented using a hash table can reduce the number of intersections needed for the execution of BAQ. The performance of the proposed technique is compared with one of the most widely used index structures for textual documents called Inverted Index. A detailed performance analysis is performed through prototyping and measurement on a system subjected to a synthetic workload. To get further performance insights, the proposed graph-based indexing technique is also compared with an enterprise-level search engine called Elasticsearch which uses Inverted Index at its core. The analysis shows that the graph-based indexing technique can reduce the latency for executing BAQ significantly in comparison to the other techniques. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BDCAT50828.2020.00019 | 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) |
Keywords | DocType | ISBN |
text indexing,keyword search,Boolean AND queries,Graph-Based Index | Conference | 978-1-6654-1567-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdulla Kalandar Mohideen | 1 | 0 | 0.34 |
| Shikharesh Majumdar | 2 | 435 | 75.95 |
| Marc St-Hilaire | 3 | 1 | 1.07 |
| Ali El-Haraki | 4 | 0 | 0.68 |