M. Amparo Vila

Ontologies versus relational databases: are they so different? A comparison

Two main data models are currently used for representing knowledge and information in computer systems. Database models, especially relational databases, have been the leader in last few decades, enabling information to be efficiently stored and queried. On the other hand, ontologies have appeared as an alternative to databases in applications that require a more ‘enriched’ meaning. However, there is controversy regarding the best information modeling technique, as both models present similar characteristics. In this paper, we present a review of how ontologies and databases are related, of what their main differences are and of the mechanisms used to communicate with each other.

Fuzzy quantification: a state of the art

Abstract Quantified sentences are a very powerful notion for modelling statements in Natural Language (NL), but in practice they have been used to solve several problems. This paper is intended to offer a global view of the development on this branch until now, focusing in the different approaches dealing with quantification, specially those involving imprecision, called fuzzy quantification. We put attention to the different mechanisms for defining them, the evaluation methods for measuring their fulfilment, as well as the properties they should satisfy.

Online recognition of human activities and adaptation to habit changes by means of learning automata and fuzzy temporal windows

Smart Homes are intelligent spaces that contain resources to collect information about users activities and ease the assisted living. Abnormal behavior detection has been remarked as one of the most challenging application fields in this research area, due to its possibilities for assisting elders or people with special needs. These systems help to maintain peoples independence, enhancing their personal comfort and safety and delaying the process of moving to a nursing home. In this paper, we describe a new approach for the behavior recognition problem based on Learning Automata and fuzzy temporal windows. Our proposal learns the normal behaviors, and uses that knowledge to recognise normal and abnormal human activities in real time. In addition, our proposal is able to adapt online to environmental variations, changes in human habits, and temporal information, defined as an interval of time when the behavior should be performed.

Correct behavior identification system in a Tagged World

This paper presents a system that is able to process the information provided by a Tagged World to identify users behavior and to produce alarms in dangerous situations. The system inputs are signals from sensors, which are used to recognize correct behavior (action sequences) by Inductive Learning, using Data Mining techniques. The inference engine is a reasoning device that is implemented by means of Regular Grammars. It permits us to control users behavior. As output, the system produces and sends alarms when a user action sequence is wrong, indicating the erroneous actions, forgotten future, and so on. To test our system, the Tagged World is supposed to be at a house, where we have used RFID technology to control the objects inside it.

The Role of Information Technologies to Address Data Handling in Construction Project Management

AbstractConstruction is an extremely information-dependent industry in which a project’s success largely depends on good access to and management of data. Effective project management requires the characterization of its challenging issues and the use of appropriate tools for data handling. For this purpose, the construction industry is increasingly adopting the use of information and communication technologies (ICT) in recent years. Given the acknowledged potential of ICT to bring about improvements in other industries, many initiatives have been undertaken to develop appropriate tools to support various tasks during the construction project lifecycle. This paper focuses on the proposals that use ICT to provide access to the data and take advantage of this access to manage crucial issues within project management such as costs, planning, risks, safety, progress monitoring, and quality control. The authors will demonstrate that suitable data handling facilitates and improves the decision-making process an...

An Approach for the Automatic Classification of Work Descriptions in Construction Projects

Project success is directly related to reliable access to accurate project data. During the construction project life cycle, an enormous amount of documents with largely interesting information are exchanged, which makes the decision-making process very complex to project managers. One of the most important documents in this context is the Bill of Quantities BoQ, which is a collection of Work Descriptions describing the nature of the different works needed to be done to achieve the project goal. As BoQs are differently built, practitioners have trouble when trying to insert them in a common database storage system, because both the linguistic descriptions and the structure of each BoQ document may be defined in a different way. In this article, we focus on this problem and present an approach to automatically extract the information from the work descriptions and to appropriately place it in a predefined hierarchical structure, independently of its original format. Our approach is based on a multicriteria aggregation process based on the use of learned information and expert knowledge. The methodology has been validated with a broad set of work descriptions. Results show that the proposal is able to classify them with success.

Similarity measure for anomaly detection and comparing human behaviors

Herein, we put forth a new similarity measure for anomaly detection and for comparing human behaviors based on the theories of learning automata, comparison of soft partitions, and temporal probabilistic order relations. In particular, focus is placed on monitoring individuals in a home setting for their own well‐being. This work is a high‐level investigation focused on the structure of human behavior. Examples demonstrate the utility of this approach for (1) understanding the similarity of pairs of behaviors for an individual (or alternatively between individuals) and (2) detecting significant change between changing behavior and a baseline model. In the context of eldercare, significant change in behavior can be a precursor to cognitive and/or functional health related problems. Simulated resident behavior is used to show different scenarios and the response of the proposed measure.

Fuzzy method to disclose behaviour patterns in a Tagged World

The main objective of the Ubiquitous computing is to include technology on the user life without modifying their daily routine. A lot of kinds of applications base their operations on using sensors situated on a Tagged World, which is a smart area that serves to recognize users behaviour using information about their daily activity. It manages information collected by sensors to identify the user behaviour and to provide some services according to inferred behaviours. In this paper, we present a specific method to extract behaviour patterns on time from the collected sensor signals. The method is based on frequent itemsets that represent the common actions of the user. After, we obtain sequence patterns from these extracted itemsets. Our method should pay attention to the user activities because these have non-random imprecision by definition. Thus, we have designed a method to handle this imprecision, through establishing a temporal constraint that is called Fuzzy Temporal Window.

F-CUBE FACTORY: A FUZZY OLAP SYSTEM FOR SUPPORTING IMPRECISION

The special needs of the OLAP technology were the main cause of the use of a multidimensional view of the data. Crisp models are not suitable to model complex or non well defined domains. They also fail to integrate data from semi/non-structured sources (e.g. Internet) or with incompatibilities in their schemata. In these situations, as a result of the modelling and/or integration, imprecision appears. So, we need a model able to manage imprecision in the structures and data. If we want to use experts knowledge in the analysis, we have to keep in mind that expert users are more comfortable when they use linguistic expressions instead of exact values. In this paper we present an extension of a fuzzy multidimensional model to support the use of linguistic labels in the definition of the hierarchies and the OLAP system that implements this model.

A Flexible Approach to the Multidimensional Model: The Fuzzy Datacube

As a result of the use of OLAP technology in new fields of knowledge and the merge of data from different sources, it has become necessary for models to support this technology. In this paper, we propose a new multidimensional model that can manage imprecision both in dimensions and facts. Consequently, the multidimensional structure is able to model data imprecision resulting from the integration of data from different sources or even information from experts, which it does by means of fuzzy logic.