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Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit.

Ingo Frommholz¹, Thomas Roelleke²

¹Institute for Research in Applicable Computing, University of Bedfordshire, Luton, UK.

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|January 26, 2018

Summary

This summary is machine-generated.

Probabilistic Datalog (PDatalog) is enhanced by the HySpirit system for real-world information retrieval. HySpirit adds probability estimation, vague predicates, and scalable processing for data management.

Keywords:

DB+IR HySpirit Probabilistic Datalog Scalability

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Area of Science:

Computer Science
Artificial Intelligence
Database Systems

Background:

Probabilistic Datalog (PDatalog) offers a logical, rule-based approach to Information Retrieval (IR).
Existing PDatalog implementations face challenges in real-world application scalability and feature integration.

Purpose of the Study:

To present key features of the HySpirit system for scaling Probabilistic Datalog execution.
To demonstrate how HySpirit addresses requirements for real-scale data management and analysis applications.

Main Methods:

Integration of probability estimation capabilities within PDatalog.
Inclusion of fuzzy-like predicates for modeling vague predicates.
Implementation of probabilistic relational indexes and parallel/distributed processing for scalability.

Main Results:

HySpirit enables the expression of probability estimation in PDatalog programs.
Vague predicates are effectively modeled using fuzzy-like predicates.
Scalable execution of PDatalog is achieved through probabilistic relational indexes and distributed computing.

Conclusions:

The HySpirit system provides a consolidated approach to making PDatalog applicable for real-scale data management.
HySpirit's features address critical requirements for probabilistic reasoning in complex data environments.