Elton Domnori

Keyword search over relational databases: a metadata approach

Keyword queries offer a convenient alternative to traditional SQL in querying relational databases with large, often unknown, schemas and instances. The challenge in answering such queries is to discover their intended semantics, construct the SQL queries that describe them and used them to retrieve the respective tuples. Existing approaches typically rely on indices built a-priori on the database content. This seriously limits their applicability if a-priori access to the database content is not possible. Examples include the on-line databases accessed through web interface, or the sources in information integration systems that operate behind wrappers with specific query capabilities. Furthermore, existing literature has not studied to its full extend the inter-dependencies across the ways the different keywords are mapped into the database values and schema elements. In this work, we describe a novel technique for translating keyword queries into SQL based on the Munkres (a.k.a. Hungarian) algorithm. Our approach not only tackles the above two limitations, but it offers significant improvements in the identification of the semantically meaningful SQL queries that describe the intended keyword query semantics. We provide details of the technique implementation and an extensive experimental evaluation.

Keymantic: semantic keyword-based searching in data integration systems

We propose the demonstration of Keymantic, a system for keyword-based searching in relational databases that does not require a-priori knowledge of instances held in a database. It finds numerous applications in situations where traditional keyword-based searching techniques are inapplicable due to the unavailability of the database contents for the construction of the required indexes.We propose the demonstration of Keymantic, a system for keyword-based searching in relational databases that does not require a-priori knowledge of instances held in a database. It finds numerous applications in situations where traditional keyword-based searching techniques are inapplicable due to the unavailability of the database contents for the construction of the required indexes.

Coordination Issues in an Agent-Based Approach for Territorial Emergence Management

This paper presents a new architectural approach for managing healthcare emergences and large scale disasters using agent-based applications. Such scenarios require a support that must be distributed, context-aware, reactive and autonomous. In the following, we will show how agent’s behaviour best fits to these requirements and how the agent technology can be efficiently applied to problems where the main challenge is coordination and collaboration between components. We propose a FIPA compliant Multi-Agent System based on the BDI and PIM technologies.

Multi-agent Approach for Disaster Management

The emergency situations deriving from natural disasters or large scale accidents represent one of the most critical activities in the health care system. We are all concerned about the necessity of a coordinated reaction and organization when there is a large number of people involved and time is a very precious aspect. This paper introduces a multi-agent system for the management of the territorial emergencies in large scale disasters and shows how the agent technology can meet successfully the related requirements. In this paper, we propose a multi-agent framework, called Ubimedic2, able to coordinate autonomously the operative units during rescue operations.

Designing and Implementing Intelligent Agents for e-Health

Recent large-scale disasters and territorial emergencies, such as the Japan seaquake and Australian inundation, have brought to the attention the efficiency of the rescue operation. Such scenarios are unpredictable and require a prompt and coordinated reaction from the authorities. This paper presents an architectural approach for coordination and cooperation based on the Multi-Agent technology. In the following, we will show how agents are able to offer a distributed, context-aware, reactive and autonomous support. We propose a Multi-Agent framework that enables the implementation of Multi-Agent Systems (MAS) for emergency scenarios management.

Implementing Agent Interoperability between Language-Heterogeneous Platforms

One of the most important feature of a MAS (Multi-Agent System) is the cooperation and the coordination among agents placed in distributed environments. Due to the high heterogeneity of agents and platforms, interoperability has become a key issue for the use of MASs in academic and industrial fields. Several approaches to interoperability proposed so far are based on the JAVA technology exploiting the homogeneity of the programming language and technology. In this paper we introduce a new middleware that enables the interoperability among agents execution in different platforms, implemented with different programming languages. Our approach is FIPA-IEEE compliant and programming-language independent.

Understanding the Semantics of Keyword Queries on Relational Data Without Accessing the Instance

The simplicity of keyword queries has made them particularly attractive to the technically unskilled user base, tending to become the de facto standard for querying on the web. Unfortunatelly, alongside its simplicity, came also the loose semantics. Researchers have, for a long time, studied ways to understand the keyword query semantics and retrieve the most relevant data artifacts. For the web, these artifacts were documents; thus, any semantics discovering effort was based mainly on statistics about the appearance of the keywords in the documents. Recently, there has been an increasing interest in publishing structural data on the web, allowing users to exploit valuable resources that have so far been kept private within companies and organizations. These sources support only structural queries. If they are to become available on the web and be queried, the queries will be in the form of keywords and they will have to be translated into structured queries in order to be executed. Existing works have exploited the instance data in order to build off-line an index that is used at query time to drive the translation. This idea is not always possible to implement since the owner of the data source is typically not willing to allow unrestricted access to the data or to offer resources for the index construction. This chapter elaborates on methods of discovering the semantics of keyword queries without requiring access to the instance data. It describes methods that exploit metainformation about the source data and the query in order to find semantic matches between the keywords and the database structures. These matches form the basis for translating the keyword query into a structure query.