Data and Knowledge Engineering

This line carries out research and teaching in database with emphasis on management of distributed data, whether spatial, complex, or fuzzy rules.

The line also develops research in Knowledge Management, is in the more technical aspects, such as data mining, is the organizational aspects relevant to all knowledge of the cycle.

Computer Supported Cooperative Work (CSCW), especially support for cooperative decision support and cooperation in science and engineering, is a research topic with enough activity on the line.

 

Main Research Themes

Data Management distributed: Web, Clusters and Grids

The main objective of the management of distributed data is in the generation of new techniques of management for three environments, namely, Web, PC Clusters and Grids, involving allocation, replication, performance management services and query processing.

Although the Web had been used intensively in the last decade, there were new technological standards such as web services (Web Services) with the XML. The composition of web services implies new form of data management, such as new transaction management techniques and processing of XML queries. In the context of clusters of PCs, the cheapening of these systems, coupled with software models for distributed memory sharing (DSM) has brought new challenges for the storage and manipulation of data with parallel processing. Finally, the computational Grids have emerged more recently to provide environments that integrate diverse computing resources that are managed by different organizations and geographically distributed. The Grids bring an innovative necessity in data management, combining the problems encountered on the Web with the problems of Clusters.

Bioinformatics applications typically manage a large volume of data, dynamic, multi-dimensional, with different levels of complexity, and from various heterogeneous sources (protein sequencing and genes, sensory data, digital imaging, among others). These characteristics require the use of advanced technology for proper management and a satisfactory performance in the handling of the stored data extraction and knowledge management from these data. These applications have been subject to review data management techniques and distributed web services.


Geographic Database Interoperability

Given that interoperability between databases is a growing need among the various institutions working with georeferenced information, this activity aims to develop tools and systems to support the integration of Geographic or Spatial databases and promote the interoperability of systems and tools that use them. Are studied also methods for semantic and physical integration and cooperation for the integration and use of data bases.


Data Mining

are conducted research in data mining, are in conventional, spatial and / or temporal data. The emphasis is the development of new algorithms and improvement algorithms already established.


Cooperation and recovery of multidimensional information

investigate the search and recovery techniques information used in the Internet, analyzing their efficiency and suitability for the user. Identify models and interfaces used to specify tools information sources handling cooperatives. Investigate the need for tools for semantic integration of visions and databases looking for interoperation of heterogeneous databases.


New Algorithms and Architectures for Distributed Spatial Databases

Systems Managers Bank Distributed Spatial Data - BDED - integrate the technology of Distributed Databases - BDD - and Spatial Databases - BDE.

The question of the processing of spatial operations in BDED has had little attention despite its central importance in BDE. In this sense are being identified, discussed and developed to address new issues that arise in processing spatial operations in spatial databases distributed systems.

Another purpose of this activity is to compare the performance of spatial structures stored in secondary memory (disk) in parallel messaging environment. Is also being explored to implement within the GOA R and PMR trees, combining the benefits of parallelism and persistence services offered by the DBMS.

Also in this topic are research on raster filters. The filter-and-refining technique for spatial processing junctions served as the starting point for many approaches that seek to improve the performance of this operation. We proposed the raster filters as an attractive alternative to pure and simple use of limiting boxes (minimum bounding rectangles) mainly in the processing of polygons collections. In our developments we extend this technique and adapt the processing of polylines.

We are developing a methodology to partition adaptively arbitrarily complex spatial objects that can be incorporated into the algorithms used in the creation of similar data structures to R-trees. It is intended therefore obtaining indexing technique that works equally well for large collections of simple polygonal objects as for small collections of complex polygonal objects.

 

Model Driven Architecture

the MDA is a new systems development methodology, which provides a high level of abstraction for the development of distributed applications. The MDA separates the task of modeling the implementation details without losing the integration between the model and the development of the application on a specific platform. The development process MDA uses the UML standards (Unified Modeling Language), MOF (Meta-Object Facility) and CWM (Common Warehouse Meta-model) to specify independent deployment platform systems. The specifications made in the MDA model can be implemented in proprietary or open platforms, if including CORBA, Java, .NET and XMI / XML.

the research line in MDA intends to propose solutions to the following questions:

  • How should the specification of the behavior of a system in order to ensure the correctness of the processing, while keeping the level of abstraction of the specification;
  • What mechanisms functionality of transformation between models should possess in order to facilitate the customization of models oriented environments; and
  • How to apply oriented development model, efficiently, in the modeling of business processes supported by Web Services

    The research is carried out in partnership with companies and government agencies, especially with the ministries of Defence, Ministry of Planning and the Army Command. In this project, different aspects of MDA are being researched, tested and successfully used in the development of a real system.

 

Architectures, technologies and solutions for the Internet of Things

The Internet of Things is the next generation of the Internet, and promises to revolutionize the way we interact with the physical and virtual worlds. IoT consists of a world of embedded physical objects with sensors and actuators, connected by wireless networks and communicating using the Internet, shaping a network of intelligent objects able to capture environmental variables and react to external stimuli. These smart objects are connected to and can be controlled by the Internet, allowing a multitude of new applications. IoT is one of the leading technologies to enable the creation of cyber physical systems and realize the vision of Smart Cities.

Several recent technological advances have allowed the emergence of IoT, such as nanotechnology, wireless sensor networks, mobile communication, and ubiquitous computing. However, there is still a set of technological and research challenges to be overcome to fully realize the IoT paradigm. These challenges are mainly related to (i) the design of solutions (such as middleware platforms) to deal with the huge heterogeneity resulting from the diversity of hardware, sensors and actuators and wireless technologies inherent to IoT, (ii) building IoT applications, considering the scale and heterogeneity of these environments; (iii) processing and storing the huge amount of data generated by IoT devices, often data streams requiring online processing, and (iv) managing the (heterogeneous) resources needed to process data and provide value-added information and responses efficiently and timely for applications.

Thus, in this research subject, new solutions will be investigated and proposed in terms of architectures, middleware platforms, data stream processing systems, and algorithms for managing and allocating resources to deal with the new challenges imposed by the IoT paradigm, thus leveraging its potential benefits.

 

Faculty

 

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