A Unit Test Approach for Database Schema Evolution

Context: The constant changes in today’s business requirements demand continuous database revisions. Hence, database structures, not unlike software applications, deteriorate during their lifespan and thus require refactoring in order to achieve a longer life span. Although unit tests support changes to application programs and refactoring, there is currently a lack of testing strategies for database schema evolution. Objective: This work examines the challenges for database schema evolution and explores the possibility of using various testing strategies to assist with schema evolution. Specifically, the work proposes a novel unit test approach for the application code that accesses databases with the objective of proactively evaluating the code against the altered database. Method: The approach was validated through the implementation of a testing framework in conjunction with a sample application and a relatively simple database schema. Although the database schema in this study was simple, it was nevertheless able to demonstrate the advantages of the proposed approach. Results: After changes in the database schema, the proposed approach found all SELECT statements as well as the majority of other statements requiring modifications in the application code. Due to its efficiency with SELECT statements, the proposed approach is expected to be more successful with database warehouse applications where SELECT statements are dominant. Conclusion: The unit test approach that accesses databases has proven to be successful in evaluating the application code against the evolved database. In particular, the approach is simple and straightforward to implement, which makes it easily adoptable in practice

Model-based programming environments for spreadsheets

Spreadsheets can be seen as a flexible programming environment. However, they lack some of the concepts of regular programming languages, such as structured data types. This can lead the user to edit the spreadsheet in a wrong way and perhaps cause corrupt or redundant data. We devised a method for extraction of a relational model from a spreadsheet and the subsequent embedding of the model back into the spreadsheet to create a model-based spreadsheet programming environment. The extraction algorithm is specific for spreadsheets since it considers particularities such as layout and column arrangement. The extracted model is used to generate formulas and visual elements that are then embedded in the spreadsheet helping the user to edit data in a correct way. We present preliminary experimental results from applying our approach to a sample of spreadsheets from the EUSES Spreadsheet Corpus. Finally, we conduct the first systematic empirical study to assess the effectiveness and efficiency of this approach. A set of spreadsheet end users worked with two different model-based spreadsheets, and we present and analyze here the results achieved.This work is funded by ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-010048. The first author is supported by the FCT grant SFRH/BPD/73358/2010

Interactive model-based decision-making tools in early product platform design

Integrating new technologies in existing product platforms presents challenges not only when the decision of going ahead with the integration is taken, but also in the earlier design of the platform structure to accommodate hypothetical changes in the future. Common heuristics do no guarantee that the optimum solution can be found to these kinds of problems, and biases lead to systematic distortions in decision-making. Additionally with the global zeitgeist around sustainable development, decision makers will increasingly ask for paths to many different versions of success, not just the traditional profit maximization one. A set of common models that accompanies the product platform all through its lifecycle to support decision makers can enable better fulfilment of the expectations of all stakeholders. But it is difficult to unify and objectively gather the views of multiple stakeholder simultaneously. An interactive modelbased decision making support system is proposed as a tool to solve the mentioned challenges. In this paper we describe and experiment with the main technological foundations of such a tool. These include an web-based front end, and a real-Time NoSQL database in the back end. The client web application (webapp) enables user inputs, runs quantitative models, and visualizes results. The database records results and enables the use of common inputs and common visualization of the results. The models that run directly in the client are developed offline and can be continuously deployed with no downtime for concurrent users. The technology stack used demonstrates that rapid prototyping of tools using state-of-The-Art web technologies provides quick results and enables researchers to make quick iterations that can be easily deployed in industrial use cases. The presented method is a new approach to providing digital support to the design process, by enabling better informed decisions during the product development process early phases. In this paper, an introduction and background to the problem and current state of the art is summarized, a method to approaching the topic is described, an experiment performed in front of a life audience is presented, and hints for future developments are considered in the discussion and conclusion sections

Differentiated Multiple Aggregations in Multidimensional Databases

International audienceMany models have been proposed for modeling multidimensional data warehouse and most consider a same function to determine how measure values are aggregated according to different data detail levels. We provide a conceptual model that supports (1) multiple aggregations, associating to the same measure a different aggregation function according to analysis axes or hierarchies, and (2) differentiated aggregation, allowing specific aggregations at each detail level. Our model is based on a graphical formalism that allows controlling the validity of aggregation functions (distributive, algebraic or holistic). We also show how conceptual modeling can be used, in an R-OLAP environment, for building lattices of pre-computed aggregates

Mining Oncology Data: Knowledge Discovery in Clinical Performance of Cancer Patients

Our goal in this research is twofold: to develop clinical performance databases of cancer patients, and to conduct data mining and machine learning studies on collected patient records. We use these studies to develop models for predicting cancer patient medical outcomes. The clinical database is developed in conjunction with surgeons and oncologists at UMass Memorial Hospital. Aspects of the database design and representation of patient narrative are discussed here. Current predictive model design in medical literature is dominated by linear and logistic regression techniques. We seek to show that novel machine learning methods can perform as well or better than these traditional techniques. Our machine learning focus for this thesis is on pancreatic cancer patients. Classification and regression prediction targets include patient survival, wellbeing scores, and disease characteristics. Information research in oncology is often constrained by type variation, missing attributes, high dimensionality, skewed class distribution, and small data sets. We compensate for these difficulties using preprocessing, meta-learning, and other algorithmic methods during data analysis. The predictive accuracy and regression error of various machine learning models are presented as results, as are t-tests comparing these to the accuracy of traditional regression methods. In most cases, it is shown that the novel machine learning prediction methods offer comparable or superior performance. We conclude with an analysis of results and discussion of future research possibilities