Continuum: an architecture for user evolvable collaborative virtual environments

Continuum is a software platform for collaborative virtual environments. Continuum\u27s architecture supplies a world model and defines how to combine object state, behavior code, and resource data into this single shared structure. The system frees distributed users from the constraints of monolithic centralized virtual world architectures and instead allows individual users to extend and evolve the virtual world by creating and controlling their own individual pieces of the larger world model. The architecture provides support for data distribution, code management, resource management, and rapid deployment through standardized viewers. This work not only provides this architecture, but it includes a proven implementation and the associated development tools to allow for creation of these worlds

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

Evolvable View Environment (EVE): Non-Equivalent View Maintenance under Schema Changes

this paper for an online demonstration of this early version of our system

Data quality maintenance in Data Integration Systems

A Data Integration System (DIS) is an information system that integrates data from a set of heterogeneous and autonomous information sources and provides it to users. Quality in these systems consists of various factors that are measured in data. Some of the usually considered ones are completeness, accuracy, accessibility, freshness, availability. In a DIS, quality factors are associated to the sources, to the extracted and transformed information, and to the information provided by the DIS to the user. At the same time, the user has the possibility of posing quality requirements associated to his data requirements. DIS Quality is considered as better, the nearer it is to the user quality requirements. DIS quality depends on data sources quality, on data transformations and on quality required by users. Therefore, DIS quality is a property that varies in function of the variations of these three other properties. The general goal of this thesis is to provide mechanisms for maintaining DIS quality at a level that satisfies the user quality requirements, minimizing the modifications to the system that are generated by quality changes. The proposal of this thesis allows constructing and maintaining a DIS that is tolerant to quality changes. This means that the DIS is constructed taking into account previsions of quality behavior, such that if changes occur according to these previsions the system is not affected at all by them. These previsions are provided by models of quality behavior of DIS data, which must be maintained up to date. With this strategy, the DIS is affected only when quality behavior models change, instead of being affected each time there is a quality variation in the system. The thesis has a probabilistic approach, which allows modeling the behavior of the quality factors at the sources and at the DIS, allows the users to state flexible quality requirements (using probabilities), and provides tools, such as certainty, mathematical expectation, etc., that help to decide which quality changes are relevant to the DIS quality. The probabilistic models are monitored in order to detect source quality changes, strategy that allows detecting changes on quality behavior and not only punctual quality changes. We propose to monitor also other DIS properties that affect its quality, and for each of these changes decide if they affect the behavior of DIS quality, taking into account DIS quality models. Finally, the probabilistic approach is also applied at the moment of determining actions to take in order to improve DIS quality. For the interpretation of DIS situation we propose to use statistics, which include, in particular, the history of the quality models

Reducing the View Selection Problem through Code Modeling: Static and Dynamic approaches

2015 - 2016Data  warehouse  systems aim to support decision making by providing users with the appropriate  information  at  the right time. This task is particularly challenging in business contexts where large  amount of data is produced at a high speed. To this end, data warehouses have been equipped with  Online Analytical Processing tools that help users to make fast and precise decisions througt the  execution of complex queries. Since the computation of these queries is time consuming, data   warehouses precompute a set of materialized views answering to the workload  queries.   This thesis work defines a process to determine the minimal set of workload queries and the set of views to materialize. The set of queries is represented by an optimized lattice structure used to select  the views to be materialized according to the processing time costs and the view storage space. The minimal set of required Online Analytical Processing queries is computer by analyzing the data model defined with the visual language CoDe (Complexity Design). The latter allows to conceptually organizatio  the visualization of data reports and to generate visualizations of data obtained from data-­‐mart queries. CoDe adopts a hybrid modeling process combining two main methodologieser-­‐driven and data-­ driven. The first aims to create a model according to  the  user  knowledge,  re-quirements, and analysis needs, whilst the latter has in  charge to concretize data  and their relationships in the model through Online Analytical Processing queries. Since the materialized views change over time, we also propose a dynamic process that allows users to upgrade the CoDe model with a context-­‐aware editor, build an optimized lattice structure able to  minimize the effort to recalculate it,and propose the new set of views  to  materialize  Moreover,  the  process applies a Markov strategy to predict whether the views need to be recalculate or not  according to the changes of the model. The effectiveness of the proposed  techniques has  been  evaluated on a real world data warehouse. The results  revealed that the Markov strategy gives a better set of solutions in term of storage space and total processing cost. [edited by author]  XV n.

An Analysis of Diversity in Genetic Programming

Genetic programming is a metaheuristic search method that uses a population of variable-length computer programs and a search strategy based on biological evolution. The idea of automatic programming has long been a goal of artificial intelligence, and genetic programming presents an intuitive method for automatically evolving programs. However, this method is not without some potential drawbacks. Search using procedural representations can be complex and inefficient. In addition, variable sized solutions can become unnecessarily large and difficult to interpret. The goal of this thesis is to understand the dynamics of genetic programming that encourages efficient and effective search. Toward this goal, the research focuses on an important property of genetic programming search: the population. The population is related to many key aspects of the genetic programming algorithm. In this programme of research, diversity is used to describe and analyse populations and their effect on search. A series of empirical investigations are carried out to better understand the genetic programming algorithm. The research begins by studying the relationship between diversity and search. The effect of increased population diversity and a metaphor of search are then examined. This is followed by an investigation into the phenomenon of increased solution size and problem difficulty. The research concludes by examining the role of diverse individuals, particularly the ability of diverse individuals to affect the search process and ways of improving the genetic programming algorithm. This thesis makes the following contributions: (1) An analysis shows the complexity of the issues of diversity and the relationship between diversity and fitness, (2) The genetic programming search process is characterised by using the concept of genetic lineages and the sampling of structures and behaviours, (3) A causal model of the varied rates of solution size increase is presented, (4) A new, tunable problem demonstrates the contribution of different population members during search, and (5) An island model is proposed to improve the search by speciating dissimilar individuals into better-suited environments. Currently, genetic programming is applied to a wide range of problems under many varied contexts. From artificial intelligence to operations research, the results presented in this thesis will benefit population-based search methods, methods based on the concepts of evolution and search methods using variable-length representations

Reusing dynamic data marts for query management in an on-demand ETL architecture

Data analysts working often have a requirement to integrate an in-house data warehouse with external datasets, especially web-based datasets. Doing so can give them important insights into their performance when compared with competitors, their industry in general on a global scale, and make predictions as to sales, providing important decision support services. The quality of these insights depends on the quality of the data imported into the analysis dataset. There is a wealth of data freely available from government sources online but little unity between data sources, leading to a requirement for a data processing layer wherein various types of quality issues and heterogeneities can be resolved. Traditionally, this is achieved with an Extract-Transform-Load (ETL) series of processes which are performed on all of the available data, in advance, in a batch process typically run outside of business hours. While this is recognized as a powerful knowledge-based support, it is very expensive to build and maintain, and is very costly to update, in the event that new data sources become available. On-demand ETL offers a solution in that data is only acquired when needed and new sources can be added as they come online. However, this form of dynamic ETL is very difficult to deliver. In this research dissertation, we explore the possibilities of creating dynamic data marts which can be created using non-warehouse data to support the inclusion of new sources. We then examine how these dynamic structures can be used for query fulfillment andhow they can support an overall on-demand query mechanism. At each step of the research and development, we employ a robust validation using a real-world data warehouse from the agricultural domain with selected Agri web sources to test the dynamic elements of the proposed architecture