Rodrigo Salvador Monteiro

Building the data warehouse of frequent itemsets in the DWFIST approach

Some data mining tasks can produce such great amounts of data that we have to cope with a new knowledge management problem. Frequent itemset mining fits in this category. Different approaches were proposed to handle or avoid somehow this problem. All of them have problems and limitations. In particular, most of them need the original data during the analysis phase, which is not feasible for data streams. The DWFIST (Data Warehouse of Frequent ItemSets Tactics) approach aims at providing a powerful environment for the analysis of itemsets and derived patterns, such as association rules, without accessing the original data during the analysis phase. This approach is based on a Data Warehouse of Frequent Itemsets. It provides frequent itemsets in a flexible and efficient way as well as a standardized logical view upon which analytical tools can be developed. This paper presents how such a data warehouse can be built.

Improving tests infrastructure through a model-based approach

Software tests have a high impact on the cost of software development. In practice, they are generally created at random and without any methodology, and do not have sufficient documentation. Commonly used approaches also perform the tests outside the application environment (e.g. web servers and containers). Besides, the test cases are usually restricted to target business components behavior, leaving a huge gap by not evaluating the presentation layer. Most of these practices can be explained by the overhead required to maintain manually the whole test artifacts. Applying a Model-based Approach (MBA), the creation and maintenance of test artifacts can be automated. This paper proposes a method that applies the Model-driven Architecture (MDA), a strategy of MBA, to determine the flow of test cases. The proposed method was based on the use of Unified Modeling Language (UML) activity diagrams. These diagrams allow determining the test flows and the objective of each activity, such as testing of business and presentation layers. Moreover, the generated test artifacts allow for performing the tests inside the application environment.

Polyline Spatial Join Evaluation Using Raster Approximation

The main subject of spatial joins is polygons and polylines. Typical polygons and polylines can occupy several Kbytes. Since approximations use much less space, the processing of spatial joins can be greatly improved by the use of filters that reduce the need for examining the exact geometry of spatial objects in order to find the intersecting ones. Candidate pairs of approximations of spatial objects are evaluated using such filters. As a result, three possible sets of answers are identified: the positive one, composed of intersecting pairs; the negative one, composed of non-intersecting pairs; and the inconclusive one, composed of the remaining pairs of candidates. There are many approximations designed for polygons, but few are suitable for approximating polylines. This paper presents a spatial join processor based on the multi step query processor (MSQP) architecture [24]. We have developed a new polyline approximation, named five color direction raster signature (5CDRS) [18]. It is used as the filter of MSQP’s second step. The performance of the join processor and the approximation was evaluated with real world data sets. The results showed that our approach, when compared to others presented in the literature, reduced the inconclusive answers by 29% in the average. Consequently, the need for retrieving the representation of polylines and carrying out exact geometry tests is reduced by the same factor. As the exact geometry test is the most time consuming step, we have noticed that the overall time is also reduced by 38% in the average. The disk accesses are reduced by 41% in the average.

Polygon and Polyline Join Using Raster Filters

Processing spatial joins efficiently is crucial to rendering the spatial data analysis process feasible. As pointed out in many works, the exact intersection test of two spatial objects is the most time-consuming and I/O-consuming step in processing spatial joins. The use of approximations can reduce the need for examining the exact geometry of spatial objects in order to find the intersecting ones. In previous works, approximations were proposed to perform spatial joins between similar objects: polygons × polygons or polylines × polylines. This work evaluates the benefits of using approximations in spatial joins performed on polygons and polylines sets. Also, a new algorithm is proposed to compare approximations of polygons and polylines. The experiments performed with real data sets resulted in performance gains validating approach effectiveness. The number of exact intersection tests was reduced by 59%. The overall execution time and number of disk accesses were both reduced by 48%.

Collaborative evolution process in MDArte: Exchanging solutions for information systems development among projects

Information Systems Development faces a lot of recurrent issues that must be addressed in every project. A lot of common requirements and features repeatedly appear on different projects challenging the development team. Exchanging solutions and the expertise gained over the evaluation of such solutions among projects can prevent the development teams from reinventing the wheel. The MDArte framework has been used to develop information systems through the Model Driven Architecture approach, automating the generation from models reaching around 80% of the application code. Most important in the present context is that the MDArte framework turned out to be a common platform between the adherent projects used to share new solutions and features. This paper presents how the MDArte framework evolves through the collaboration of the adherent projects. It also provides examples of features developed and evaluated by one project, shared and applied in other projects. This level of collaboration between different projects on one hand accelerates the development and on the other hand prevents from neglecting important issues on information systems development.

DWFIST: leveraging calendar-based pattern mining in data streams

Calendar-based pattern mining aims at identifying patterns on specific calendar partitions. Potential calendar partitions are for example: every Monday, every first working day of each month, every holiday. Providing flexible mining capabilities for calendar-based partitions is especially challenging in a data stream scenario. The calendar partitions of interest are not known a priori and at each point in time only a subset of the detailed data is available. We show how a data warehouse approach can be applied to this problem. The data warehouse that keeps track of frequent itemsets holding on different partitions of the original stream has low storage requirements. Nevertheless, it allows to derive sets of patterns that are complete and precise. This work demonstrates the effectiveness of our approach by a series of experiments.