Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Historical collaborative geocoding

The latest developments in digital have provided large data sets that can increasingly easily be accessed and used. These data sets often contain indirect localisation information, such as historical addresses. Historical geocoding is the process of transforming the indirect localisation information to direct localisation that can be placed on a map, which enables spatial analysis and cross-referencing. Many efficient geocoders exist for current addresses, but they do not deal with the temporal aspect and are based on a strict hierarchy (..., city, street, house number) that is hard or impossible to use with historical data. Indeed historical data are full of uncertainties (temporal aspect, semantic aspect, spatial precision, confidence in historical source, ...) that can not be resolved, as there is no way to go back in time to check. We propose an open source, open data, extensible solution for geocoding that is based on the building of gazetteers composed of geohistorical objects extracted from historical topographical maps. Once the gazetteers are available, geocoding an historical address is a matter of finding the geohistorical object in the gazetteers that is the best match to the historical address. The matching criteriae are customisable and include several dimensions (fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is to facilitate historical work, we also propose web-based user interfaces that help geocode (one address or batch mode) and display over current or historical topographical maps, so that they can be checked and collaboratively edited. The system is tested on Paris city for the 19-20th centuries, shows high returns rate and is fast enough to be used interactively.Comment: WORKING PAPE

Tracking Data Provenance of Archaeological Temporal Information in Presence of Uncertainty

The interpretation process is one of the main tasks performed by archaeologists who, starting from ground data about evidences and findings, incrementally derive knowledge about ancient objects or events. Very often more than one archaeologist contributes in different time instants to discover details about the same finding and thus, it is important to keep track of history and provenance of the overall knowledge discovery process. To this aim, we propose a model and a set of derivation rules for tracking and refining data provenance during the archaeological interpretation process. In particular, among all the possible interpretation activities, we concentrate on the one concerning the dating that archaeologists perform to assign one or more time intervals to a finding to define its lifespan on the temporal axis. In this context, we propose a framework to represent and derive updated provenance data about temporal information after the mentioned derivation process. Archaeological data, and in particular their temporal dimension, are typically vague, since many different interpretations can coexist, thus, we will use Fuzzy Logic to assign a degree of confidence to values and Fuzzy Temporal Constraint Networks to model relationships between dating of different findings represented as a graph-based dataset. The derivation rules used to infer more precise temporal intervals are enriched to manage also provenance information and their following updates after a derivation step. A MapReduce version of the path consistency algorithm is also proposed to improve the efficiency of the refining process on big graph-based datasets