Open issues in semantic query optimization in relational DBMS

After two decades of research into Semantic Query Optimization (SQO) there is clear agreement as to the efficacy of SQO. However, although there are some experimental implementations there are still no commercial implementations. We first present a thorough analysis of research into SQO. We identify three problems which inhibit the effective use of SQO in Relational Database Management Systems(RDBMS). We then propose solutions to these problems and describe first steps towards the implementation of an effective semantic query optimizer for relational databases

From hard data to soft decision

It is impossible to create model of decision process, as we know nothing about the original decision process. Although it is possible to build models that can get us to the spaces where our fitness is strong enough. These models can contain hard data and soft information as well. In the background of the widely accepted solutions there are transformations of soft information into hard data. This leads us to the world of quantitative decision support. This step is very dangerous! The decision maker uses logic not arithmetic in his thinking process. DoctuS© Knowledge-Based System uses logic. The latest version is also capable of data mining. Using a clusteranalyzing algorithm it can transform the relations between hard data into soft information, which will be used for deduction in reasoning. The number of clusters is given by the user. The cluster-analyzing algorithm makes the clusters using learning example. When running the data mining the clusters remains unchanged and the new data will be transformed. The clusters can be handled using logic. For illustration we use an example of taking decision about location for a power plant

improving query performance using distributed computing

Data warehouses are used to store large amounts of data. This data is often used for On-Line Analytical Processing (OLAP) where short response times are essential for on-line decision support. One of the most important requirements of a data warehouse server is the query performance. The principal aspect from the user perspective is how quickly the server processes a given query: “the data warehouse must be fast”. The main focus of our research is finding adequate solutions to improve query response time of typical OLAP queries and improve scalability using a distributed computation environment that takes advantage of characteristics specific to the OLAP context. Our proposal provides very good performance and scalability even on huge data warehouses

Training Competences in Industrial Risk Prevention with Lego (R) Serious Play (R): A Case Study

This paper proposes the use of the Lego (R) Serious Play (R) (LSP) methodology as a facilitating tool for the introduction of competences for Industrial Risk Prevention by engineering students from the industrial branch (electrical, electronic, mechanical and technological engineering), presenting the results obtained in the Universities of Cadiz and Seville in the academic years 2017-2019. Current Spanish legislation does not reserve any special legal attribution, nor does it require specific competence in occupational risk prevention for the regulated profession of a technical industrial engineer (Order CIN 351:2009), and only does so in a generic way for that of an industrial engineer (Order CIN 311:2009). However, these universities consider the training in occupational health and safety for these future graduates as an essential objective in order to develop them for their careers in the industry. The approach is based on a series of challenges proposed (risk assessments, safety inspections, accident investigations and fire protection measures, among others), thanks to the use of "gamification" dynamics with Lego (R) Serious Play (R). In order to carry the training out, a set of specific variables (industrial sector, legal and regulatory framework, business organization and production system), and transversal ones (leadership, teamwork, critical thinking and communication), are incorporated. Through group models, it is possible to identify dangerous situations, establish causes, share and discuss alternative proposals and analyze the economic, environmental and organizational impact of the technical solutions studied, as well as take the appropriate decisions, in a creative, stimulating, inclusive and innovative context. In this way, the theoretical knowledge which is acquired is applied to improve safety and health at work and foster the prevention of occupational risks, promoting the commitment, effort, motivation and proactive participation of the student teams

Intelligent business decisions by EFLOW portal

Bigger and faster changes in business knowledge are expected. Decision maker needs knowledge (hard data and soft information) of various expertises available at deep levels of organizational hierarchy. Experts are forced to life-long learning. It is of crucial importance to use the freshest knowledge but it is even more important to avoid the false knowledge. The keystones of internet-base knowledge increase are the personalization, the freshness and to answer the question “who to learn from”. Freshness is provided by the Internet but the other two are to be handled. The eFLOW Intelligent Portal is continuously personalized, provides space for knowledge creation, enables data mining. Its portlets have multiple interconnections and they are also connected to organiza-tional databases to get hard data and to knowledge bases to get soft information. Doctus knowledge-based expert system shell embodies the built-in artificial intelligence