Search

In this paper, we present our work on a framework towards the modeling and optimization of Extraction-Transformation-Loading (ETL) workflows. The goal of this research was to facilitate, manage, and optimize the design and implementation of the ETL workflows both during the initial design and deployment stage, as well as, during the continuous evolution of a data warehouse. In particular, we present our results which include: (a) the provision of a novel conceptual model for the tracing of inter-attribute relationships and the respective ETL transformations in the early stages of a data warehouse project, along with an attempt to use ontology-based mechanisms to semi-automatically capture the semantics and the relationships among the various sources; (b) the provision of a novel logical model for the representation of ETL workflows with two main characteristics: genericity and customization; (c) the semi-automatic transition from the conceptual to the logical model for ETL workflows; and (d) the tuning of an ETL workflow for the optimization of the execution order of its operations. Finally, we discuss some issues on future work in the area that we consider important and a step towards the incorporation of the above research results to other areas as well. [ABSTRACT FROM AUTHOR]

Users without knowledge of schemas or query languages have difficulties in accessing information stored in databases. Commercial and research efforts have focused on keyword-based searches. Among them, précis queries generate entire multi-relation databases, which are logical subsets of existing ones, instead of individual relations. The logical database subset contains not only items directly related to the query selections but also items implicitly related to them in various ways. Earlier work has identified the need of providing the naïve user with meaningful answers to his questions and has suggested the translation of précis query answer in narrative form. In this paper, we present a semi-automatic method that translates the relational output of a précis query into a synthesis of results. We describe a translator engine that uses a template mechanism for generating a précis in a narrative form through a set of reusable templates. [ABSTRACT FROM AUTHOR]

Libraries, museums, and other organizations make their electronic contents available to a growing number of users on the Web. A large fraction of the information published is stored in structured or semi-structured form. However, most users have no specific knowledge of schemas or structured query languages for accessing information stored in (relational or XML) databases. Under these circumstances, the need for facilitating access to information stored in databases becomes increasingly more important. Précis queries are free-form queries that instead of simply locating and connecting values in tables, they also consider information around these values that may be related to them. Therefore, the answer to a précis query might also contain information found in other parts of the database. In this paper, we describe a précis query answering prototype system that generates personalized presentation of short factual information précis in response to keyword queries. [ABSTRACT FROM AUTHOR]

Users without knowledge of schemas or structured query languages have difficulties in accessing information stored in databases. Commercial and research efforts have focused on keyword-based searches. Among them, précis queries generate entire multi-relation databases, which are logical subsets of existing ones, instead of individual relations. A logical database subset contains not only items directly related to the query selections but also items implicitly related to them in various ways. Existing approaches to précis query answering assume that a database is pre-annotated with a set of weights, and when a query is issued, an ad-hoc logical subset is constructed on the fly. This approach has several limitations, such as dependence on users for providing appropriate weights and constraints for answering précis queries, and difficulty to capture different query semantics and user preferences. In this paper, we propose a pattern-based approach to logical database subset generation. Patterns of logical subsets corresponding to different queries or user preferences may be recognized and stored in the system. Each time a user poses a question, the system searches in a repository of précis patterns to extract an appropriate one. Then, this is enriched with tuples extracted from the database, in order to produce the logical database subset. [ABSTRACT FROM AUTHOR]

Extract-Transform-Load (ETL) workflows are data centric workflows responsible for transferring, cleaning, and loading data from their respective sources to the warehouse. In this paper, we build upon existing graph-based modeling techniques that treat ETL workflows as graphs by (a) extending the activity semantics to incorporate negation, aggregation and self-joins, (b) complementing querying semantics with insertions, deletions and updates, and (c) transforming the graph to allow zoom-in/out at multiple levels of abstraction (i.e., passing from the detailed description of the graph at the attribute level to more compact variants involving programs, relations and queries and vice-versa). [ABSTRACT FROM AUTHOR]

Extract-Transform-Load (ETL) workflows are data centric workflows responsible for transferring, cleaning, and loading data from their respective sources to the warehouse. Previous research has identified graph-based techniques that construct the blueprints for the structure of such workflows. In this paper, we extend existing results by explicitly incorporating the internal semantics of each activity in the workflow graph. Apart from the value that blueprints have per se, we exploit our modeling to introduce rigorous techniques for the measurement of ETL workflows. To this end, we build upon an existing formal framework for software quality metrics and formally prove how our quality measures fit within this framework. [ABSTRACT FROM AUTHOR]

Security incident management is one of the critical areas that offers valuable information to security experts, but still lacks much development. Currently, several security incident database models have been proposed and used. The discrepancies of such databases entail that worldwide incident information is stored in different formats and places and, so, do not provide any means for Computer Security Incident Response Teams (CSIRTs) collaboration. This paper presents an architecture based on advance database techniques, able to collect incident related information from different sources. Our framework enhances the incident management process by allowing the law enforcement units to (a) collect the required evidence from incident data that are spread through a number of different incident management systems; (b) transform, clean, and homogenize them; and, finally, (c) load them to a central database management system. Such architecture can also be beneficial by minimizing the mean time between the appearance of a new incident and its publication to the worldwide community. [ABSTRACT FROM AUTHOR]