15 research outputs found
A BELIEF-DRIVEN DISCOVERY FRAMEWORK BASED ON DATA MONITORING AND TRIGGERING
A new knowledge-discovery framework, called Data Monitoring and Discovery Triggering (DMDT),
is defined, where the user specifies monitors that âwatch" for significant changes to the data
and changes to the user-defined system of beliefs. Once these changes are detected, knowledge
discovery processes, in the form of data mining queries, are triggered. The proposed framework
is the result of an observation, made in the previous work of the authors, that when changes to
the user-defined beliefs occur, this means that, there are interesting patterns in the data. In this
paper, we present an approach for finding these interesting patterns using data monitoring and
belief-driven discovery techniques. Our approach is especially useful in those applications where
data changes rapidly with time, as in some of the On-Line Transaction Processing (OLTP) systems. The proposed approach integrates active databases, data mining queries and subjective
measures of interestingness based on user-defined systems of beliefs in a novel and synergetic
way to yield a new type of data mining systems.Information Systems Working Papers Serie
Using Ontologies for Semantic Data Integration
While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed
Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach
An approach to defining actionability as a measure of
interestingness of patterns is proposed. This approach
is based on the concept of an action hierarchy which
is defined as a tree of actions with patterns and pattern
templates (data mining queries) assigned to its
nodes. A method for discovering actionable patterns
is presented and various techniques for optimizing the
discovery process are proposed.Information Systems Working Papers Serie
Context based querying of scientific data: changing querying paradigms?
We are investigating and applying a semantically enhanced query answering machine for the needs of addressing semantically meaningful data and operations within a scientific information system. We illustrate a context based
querying paradigm on the basis of a Regional Avalanche Information and Forecasting System - RAIFoS which is concerned with the collection and analysis of snow and weather related physical parameters in the Swiss Alps. The querying paradigm relies upon the issue of interactively constructing a semantically valid query rather than formulating one in a database specific query language
and for a particular implementation model. In order to achieve this goal, the query answering machine has to make inferences concerning the properties and value domains, as well as data analysis operations, which are semantically valid within particular contexts. These inferences take place when the intended query is being constructed interactively on a Web-based blackboard. A graph-based display presentation formalism is used with elements including natural language terms, measurement units, statistical quantifiers and/or specific value domains.
A meta-data database is used to organise and provide the elements of the graph each time the graph, and consequently the intended query, is expanded or further refined. Finally, the displayed graph is transformed into elements of the implementation model from which, in turn, SQL statements and/or sequences of statistical operations are created
ConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery
Il contributo di questa tesi è il disegno e lo sviluppo di un sistema di Knoledge Discovery denominato ConQueSt.
Basato sul paradigma del Pattern Discovery guidato dai vincoli, ConQueSt segue la visione dell’Inductive Database:
• il mining è visto come forma più complessa di querying,
• il sistema quindi è equipaggiato con un data mining query language, e strettamente collegato con un DBMS
• i pattern estratti con query di mining diventano cittadini di prima classe e, seguendo il principio di chiusura, vengono materializzati accanto ai dati nel DBMS.
ConQueSt è già stato presentato con successo al workshop internazionale della comunità IDB, e alla prestigiosa conferenza IEEE International Conference on Data Mining Engineering (ICDE 2006). A giugno sarà presentato alla conferenaz italiana di basi di dati (SEBD 2006). E’ attualmente in corso la sottomissione ad una prestigiosa rivista