Probabilistic Ranking Techniques in Relational Databases by Ihab Ilyas
Ranking queries are widely used in data exploration, data analysis and decision making scenarios. We also discuss supporting rank join queries on uncertain data, and we show how to extend current rank join methods to handle uncertainty in scoring attributes.-
Datalog and Logic Databases
-
Blockchain-Enabled Large-Scale Transaction Management
-
Query Processing over Incomplete Databases
-
On Uncertain Graphs
-
Big Data Integration
-
An Introduction to Duplicate Detection
-
Full-Text (Substring) Indexes in External Memory
-
Data-Intensive Workflow Management
-
Database Replication
-
Data Protection from Insider Threats
-
Transaction Processing on Modern Hardware
-
Multidimensional Databases and Data Warehousing
-
Scalable Processing of Spatial-Keyword Queries
-
Generating Plans from Proofs
-
Query Processing over Uncertain Databases
-
Relational and XML Data Exchange
-
The Four Generations of Entity Resolution
-
Advanced Metasearch Engine Technology
-
Privacy-Preserving Data Publishing
-
Data Profiling
-
Non-Volatile Memory Database Management Systems
-
Skylines and Other Dominance-Based Queries
-
Peer-to-Peer Data Management
-
Web Page Recommendation Models
-
Cloud-Based RDF Data Management
-
User-Centered Data Management
-
Uncertain Schema Matching
-
Data Exploration Using Example-Based Methods
-
Querying Graphs
-
Access Control in Data Management Systems
-
Keyword Search in Databases
-
Community Search over Big Graphs
-
Human Interaction with Graphs
-
Data Management in Machine Learning Systems
-
Natural Language Data Management and Interfaces
-
Data Cleaning
-
Blockchains
-
Fault-Tolerant Distributed Transactions on Blockchain
-
Similarity Joins in Relational Database Systems
-
Query Answer Authentication
-
Semantics Empowered Web 3.0
-
Foundations of Data Quality Management
-
Business Processes
-
Information and Influence Propagation in Social Networks
-
Incomplete Data and Data Dependencies in Relational Databases
-
Deep Web Query Interface Understanding and Integration
-
Probabilistic Databases
Ihab F. Ilyas is an Associate Professor of Computer Science at the University of Waterloo. He received his PhD in computer science from Purdue University, West Lafayette, in 2004. He holds BS and MS degrees in computer science from Alexandria University, Egypt. His main research is in the area of database systems, with special interest in top-k and rank-aware query processing, managing uncertain and probabilistic databases, self-managing databases, indexing techniques, and spatial databases. Mohamed A. Soliman is a software engineer at Greenplum, where he works on building massively distributed database systems for efficient support of data warehousing and analytics. He received his PhD in computer science from University of Waterloo in 2010. He holds BS and MS degrees in computer science from Alexandria University, Egypt. His main research is in the area of rank-aware retrieval in relational databases, focusing primarily on supporting ranking queries on uncertain and probabilistic data.
| SKU | Unavailable |
| ISBN 13 | 9783031007187 |
| ISBN 10 | 3031007182 |
| Title | Probabilistic Ranking Techniques in Relational Databases |
| Author | Ihab Ilyas |
| Series | Synthesis Lectures On Data Management |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2011-03-21 |
| Number of pages | 71 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |