δ-information reducts and bireducts

Attribute reduction is an important step in order to decrease the computational complexity to derive information from databases. In this paper, we extend the notions of reducts and bireducts introduced in rough set theory for attribute reduction purposes and let them work with similarity relations defined on attribute values. Hence, the related mathematical concepts will be introduced and the characterizations of the new reducts and bireducts will be given in terms of the corresponding generalizations of the discernibility function.La reducción en atributos es un paso importante para disminuir la complejidad computacional para obtener información de una base de datos. En este trabajo, extendemos la noción de reductos y birredcutos introducidos en Teoría de Conjuntos Rugosos para reducción de atributos y trabajamos con relaciones de similaridad definidas en los valores de los atributos. Luego, los conceptos matemáticos relacionados se introducirán junto con las caracterizaciones de los nuevos reductos y birreductos en términos de la función de discernibilidad

Diamond Dicing

In OLAP, analysts often select an interesting sample of the data. For example, an analyst might focus on products bringing revenues of at least 100 000 dollars, or on shops having sales greater than 400 000 dollars. However, current systems do not allow the application of both of these thresholds simultaneously, selecting products and shops satisfying both thresholds. For such purposes, we introduce the diamond cube operator, filling a gap among existing data warehouse operations. Because of the interaction between dimensions the computation of diamond cubes is challenging. We compare and test various algorithms on large data sets of more than 100 million facts. We find that while it is possible to implement diamonds in SQL, it is inefficient. Indeed, our custom implementation can be a hundred times faster than popular database engines (including a row-store and a column-store).Comment: 29 page

Automatic Physical Database Tuning Middleware for Web-based Applications

Abstract. In this paper we conceptualize the database layout problem as a state space search problem. A state is a given assignment of tables to computer servers. We begin with a database and collect, for use as a workload input, a sequence of queries that were executed during normal usage of the database. The operators in the search are to fully replicate, horizontally partition, vertically partition, and de-normalize a table. We do a time intensive search over different table layouts, and at each iteration, physically create the configurations, and evaluate the total throughput of the system. We report our empirical results of two forms. First, we empirically validate as facts the heuristics that Database Administrators (DBAs) currently use as in doing this task manually: for tables that have a high ratio of update, delete, and insert to retrieval queries one should horizontally partition, but for a small ratio one should fully replicate a table. Such rules of thumb are reasonable, however we want to parameterize some common guidelines that DBAs can use. Our second empirical result is that we applied this search to our existing data test case and found a reliable increase in total system throughput. The search over layouts is very expensive, but we argue that our method is practical and useful, as entities trying to scale up their Web-based applications would be perfectly happy to spend a few weeks of CPU time to increase their system throughput (and potentially reduce the investment in hardware). To make this search more practical, we want to learn reasonable rules to guide the search to eliminate many layout configurations that are not very likely to succeed. The second aspect of our project (not reported here) is to use the created configurations as input into a machine learning system, to create general rules about when to use the different layout operators. Keywords: Database tuning, partitioning, layout search, Web-based applications

Zebrafish-based discovery of antiseizure compounds from the north sea: Isoquinoline alkaloids TMC-120A and TMC-120B

There is a high need for the development of new and improved antiseizure drugs (ASDs) to treat epilepsy. Despite the potential of marine natural products (MNPs), the EU marine biodiscovery consortium PharmaSea has made the only effort to date to perform ASD discovery based on large-scale screening of MNPs. To this end, the embryonic zebrafish photomotor response assay and the larval zebrafish pentylenetetrazole (PTZ) model were used to screen MNP extracts for neuroactivity and antiseizure activity, respectively. Here we report the identification of the two known isoquinoline alkaloids TMC-120A and TMC-120B as novel antiseizure compounds, which were isolated by bioactivity-guided purification from the marine-derived fungus Aspergillus insuetus. TMC-120A and TMC-120B were observed to significantly lower PTZ-induced seizures and epileptiform brain activity in the larval zebrafish PTZ seizure model. In addition, their structural analogues TMC-120C, penicisochroman G, and ustusorane B were isolated and also significantly lowered PTZ-induced seizures. Finally, TMC-120A and TMC-120B were investigated in a mouse model of drug-resistant focal seizures. Compound treatment significantly shortened the seizure duration, thereby confirming their antiseizure activity. These data underscore the possibility to translate findings in zebrafish to mice in the field of epilepsy and the potential of the marine environment for ASD discovery