1,721,150 research outputs found
Hybrid approach to path planning in autonomous agents
No full textThis paper focuses on the integration of symbolic and sub-symbolic knowledge for the execution of path-planning tasks in autonomous agents. Environmental knowledge is represented through a multilayered architecture whose different abstraction levels are identified by means of meta-knowledge for classification and clustering of distinctive places. The path-planning problem we consider consists in determining the cheapest path for visiting a set of resources in the environment, each resource being expressed as either a cluster or a category of clusters at any abstraction level. Time windows and precedence constraints between resources are taken into account. The algorithm we propose finds a sub-optimal solution to this problem by decomposing it at the different abstraction levels through a divide-et-impera technique
Medical decision support in clinical record management systems
No full textThe clinical record is a primary
support for the diagnostic process performed
by physicians. The development of a Clinical
Record Management System can
significantly improve the quality of patient
care by properly supporting the physicians'
tasks and, in particular, the diagnostic
process. In this paper we address some key
issues in designing a medical decision
support system. Acquisition of medical
knowledge is supported by a knowledge
editor based on a conceptual model of
diseases, aetiologies, evidence and their
relationships. Diagnostic support is carried
out by an algorithm which, according to the
production rules generated by the knowledge
editor and to the patient's clinical data,
formulates a set of diagnostic hypotheses and
suggests tests and examinations for their
validation. The definition of an internal
model for the clinical record allows for
clinical data from different departments to
be integrated. On the other hand, the
definition of external models allows for
custom presentations of clinical data to be
create
What-If Analysis
No full textIn order to be able to evaluate beforehand the impact of a strategic or tactical move so as to plan optimal strategies to reach their goals, decision makers need reliable predictive systems. What-if analysis is a data-intensive simulation whose goal is to inspect the behavior of a complex system, such as the corporate business or a part of it, under some given hypotheses called scenarios. In particular, what-if analysis measures how changes in a set of independent variables impact a set of dependent variables with reference to a given simulation model. This model is a simplified representation of the business, tuned according to the historical corporate data. In practice, formulating a scenario enables the building of a hypothetical world that the analyst can then query and navigate
Business Intelligence
No full textBusiness intelligence (often referred to as BI) is a business management term that indicates the capability of adding more intelligence to the way business is done by companies. More precisely, it refers to a set of tools and techniques that enable a company to transform its business data into timely and accurate information for the decisional process, to be made available to the right persons in the most suitable form. Business intelligence systems are used by decision makers to get a comprehensive knowledge of the business and of the factors that affect it, as well as to define and support their business strategies. The goal is to enable data-based decisions aimed at gaining competitive advantage, improving operative performance, responding more quickly to changes, increasing profitability and, in general, creating added value for the company
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Foreword
No full text
Designing web warehouses from XML schemas
No full textWeb warehousing plays a key role in providing the managers with up-to-date and comprehensive information about their business domain. On the other hand, since XML is now a standard de facto for the exchange of semi-structured data, integrating XML data into web warehouses is a hot topic. In this paper we propose a semi-automated methodology for designing web warehouses from XML sources modeled by XML Schemas. In the proposed methodology, design is carried out by first creating a schema graph, then navigating its arcs in order to derive a correct multidimensional representation. Differently from previous approaches in the literature, particular relevance is given to the problem of detecting shared hierarchies and convergence of dependencies, and of modeling many-to-many relationships. The approach is implemented in a prototype that reads an XML Schema and produces in output the logical schema of the warehouse. © Springer-Verlag Berlin Heidelberg 2003
From Star Schemas to Big Data: 20+ Years of Data Warehouse Research
No full textData Warehouses are the core of the modern systems for decision making. They store integrated information extracted from various and heterogeneous data sources, making it available in multidimensional form for analyses aimed at improving the users' knowledge of their business. Though the first use of the term dates back to the 80s, only during the late 90s data warehousing has emerged as a research area on its own, though in strict correlation with several other research topics as database integration, view materialization, data visualization, etc. This paper surveys more than 20 years of research on data warehouse systems, from their early relational implementations (still widely adopted in corporate environments), to the new architectures solicited by Business Intelligence 2.0 scenarios during the last decade, and up to the exciting challenges now posed by the integration with big data settings. The timeline of research is organized into three interrelated tracks: techniques, architectures, and methodologies
Schema Profiling of Document Stores
Full text linkIn document stores, schema is a soft concept and the documents in a collection can have different schemata; this gives designers and implementers augmented flexibility but requires an extra effort to understand the rules that drove the use of alternative schemata when heterogeneous documents are to be analyzed or integrated. In this paper we outline a technique, called schema profiling, to explain the schema variants within a collection in document stores by capturing the hidden rules explaining the use of these variants; we express these rules in the form of a decision tree, called schema profile, whose main feature is the coexistence of value-based and schema-based conditions. Consistently with the requirements we elicited from real users, we aim at creating explicative, precise, and concise schema profiles; to quantitatively assess these qualities we introduce a novel measure of entropy
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