Angelo Cesar Colombini

MR-Radix: a multi-relational data mining algorithm

BackgroundOnce multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach.MethodsAiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix.ResultsThis work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine.ConclusionThe comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

A Fast Access Big Data Approach for Configurable and Scalable Object Storage Enabling Mixed Fault-Tolerance

The progressive growth in the volume of digital data has become a technological challenge of great interest in the field of computer science. That comes because, with the spread of personal computers and networks worldwide, content generation is taking larger proportions and very different formats from what had been usual until then. To analyze and extract relevant knowledge from these masses of complex and large volume data is particularly interesting, but before that, it is necessary to develop techniques to encourage their resilient storage. Very often, storage systems use a replication scheme for preserving the integrity of stored data. This involves generating copies of all information that, if lost by individual hardware failures inherent in any massive storage infrastructure, do not compromise access to what was stored. However, it was realized that accommodate such copies requires a real storage space often much greater than the information would originally occupy. Because of that, there is error correction codes, or erasure codes, which has been used with a mathematical approach considerably more refined than the simple replication, generating a smaller storage overhead than their predecessors techniques. The contribution of this work is a fully decentralized storage strategy that, on average, presents performance improvements of over 80% in access latency for both replicated and encoded data, while minimizing by 55% the overhead for a terabyte-sized dataset when encoded and compared to related works of the literature.

A Configurable Strategy for Extraction, Transformation and Load to Support Data Propagation on Active Data Warehouses

This work consists of the construction of a strategy called ETL-PoCon to execute Extraction, Transformation and Load (ETL) processes in active Data Warehouses (DW) with a configurable policy. The original contribution of this work is to provide a strategy that considerably reduces the quantity of data transfers to active DW, besides maintaining a satisfactory level of data freshness. Said reduction is obtained by means of configurable policies of data propagation based on relevance of the data regarding to the information stored in the DW. The strategy was implemented in a database related to health worker that contains more than seventy thousand records of occupational accidents. Experiments have shown that the ETL-PoCon strategy significantly contributes towards a reduction of the overload on the systems involved in the active DW environment, since all results presented a reduction higher than 60% in the amount of DW refreshments.

CHSMST+: An Algorithm for Spatial Clustering

Spatial clustering has been widely studied due to its application in several areas. However, the algorithms of such technique still need to overcome several challenges to achieve satisfactory results on a timely basis. This work presents an algorithm for spatial clustering based on CHSMST, which allows: data clustering considering both distance and similarity, enabling to correlate spatial and nonspatial data, user interaction is not necessary, and use of multithreading technique to improve the performance. The algorithm was tested ia a real database of health area.

OntoSDM: An Approach to Improve Quality on Spatial Data Mining Algorithms

The increase in new electronic devices had generated a considerable increase in obtaining spatial data information; hence these data are becoming more and more widely used. As well as for conventional data, spatial data need to be analyzed so interesting information can be retrieved from them. Therefore, data clustering techniques can be used to extract clusters of a set of spatial data. However, current approaches do not consider the implicit semantics that exist between a region and an objects attributes. This paper presents an approach that enhances spatial data mining process, so they can use the semantic that exists within a region. A framework was developed, OntoSDM, which enables spatial data mining algorithms to communicate with ontologies in order to enhance the algorithms result. The experiments demonstrated a semantically improved result, generating more interesting clusters, therefore reducing manual analysis work of an expert.

Prediction of Spatial and Temporal Data: A Web Tool Based on Georeferenced Resources

Spatiotemporal data stored in geographic databases provide an evolutionary panorama about the characteristics of a specific region. With integration of prediction concepts and statistical functions to that data, it is possible to make inferences of obtained information, to support in many areas such as management of occupational health, environmental resources, quality of life and, others. In this article is proposed a strategy to calculate the locality of predicted spatial points with the temporal and statistic function series, which will be able to find regions with critical levels. In the concentrations of more dense occurrences, this strategy supports to choose prevention methods and offers a prediction analysis based on georeferenced resources. This work contributes towards to prediction, analysis and visualization of georeferenced data to reduce costs and improve the life quality.

The Storage System for a Multimedia Data Manager Kernel

One way to boost the performance of a Database Management System (DBMS) is by fetching data in advance of their use, a technique known as prefetching. However, depending on the resource being used (file, disk partition, memory, etc.), the way prefetching is done might be different or even not necessary, forcing a DBMS to be aware of the underlying Storage System. In this paper we propose a Storage System that frees the DBMS of this task by exposing the database through a unique interface, no matter what kind of resource hosts it. We have implemented a file resource that recognizes and exploits sequential access patterns that emerge over time to prefetch adjacent blocks to the requested ones. Our approach is speculative because it considers past accesses, but it also considers hints from the upper layers of the DBMS, which must specify the access context in which a read operation takes place. The informed access context is then mapped to one of the available channels in the file resource, which is equipped with a set of internal buffers, one per channel, for the management of fetched and prefetched data. Prefetched data are moved to the main cache of the DBMS only if really requested by the application, which helps to avoid cache pollution. So, we slightly introduced a two level cache hierarchy without any intervention of the DBMS kernel. We ran the tests with different buffer settings and compared the results against the OBL policy, which showed that it is possible to get a read time up to two times faster in a highly concurrent environment without sacrificing the performance when the system is not under intensive workloads.