Abstract SQL-on-Hadoop processing engines have become state-of-the art, yet the skills required to tune these systems are rare in the job market. Automated tuning advisers can profile the low-level MapReduce jobs and propose appropriate tuning setups, but up-front tuning is time consuming and costly. In this demo, we present DejaVu. DejaVu integrates with Hive and effectively reduces the tuning costs by... SQL-on-Hadoop processing engines have become state-of-the art, yet the skills required to tune these systems are rare in the job market. Automated tuning advisers can profile the low-level MapReduce jobs and propose appropriate tuning setups, but up-front tuning is time consuming and costly. In this demo, we present DejaVu. DejaVu integrates with Hive and effectively reduces the tuning costs by caching tuning setups for partial query plans: When the SQLon-Hadoop engine Hive compiles SQL queries into physical query plans, single MapReduce jobs tend to be similar between query plans. By recycling the tuning setups for similar low-level MapReduce jobs, DejaVu can effectively cut down the time spent profiling the TPC-H query workload in half, achieving similar impact on the performance of the jobs. While we employ Starfish in this demo, DejaVu can leverage any third-party MapReduce tuning adviser. Read More Read Less

Abstract The Web has evolved to a large variety of data usually published in RDF from multiple domains. A recurrent problem in recent literature concerns to perform a search over RDF instead of using structured queries in triple-pattern-based languages like SPARQL, which only expert programmers can precisely specify their information needs. In this paper, we propose Von-QBE, an open source tool... The Web has evolved to a large variety of data usually published in RDF from multiple domains. A recurrent problem in recent literature concerns to perform a search over RDF instead of using structured queries in triple-pattern-based languages like SPARQL, which only expert programmers can precisely specify their information needs. In this paper, we propose Von-QBE, an open source tool to query over RDF databases without any technical knowledge about RDF or the queried ontology structure. This differs from the-state-of-art tools by being schema-based instead of instance-based. It can be impracticable to use instance-based approaches in big data scenarios where the RDF data is huge and demands lots of computational resources to keep the knowledge base in memory. Moreover, most of these solutions need the knowledge base materialized into RDF(or triplified), which can be costly for legacy bases. We present various demonstration scenarios using the IMDB movie ontology. Read More Read Less

Abstract Digitalisation has been the talk of the town since the invent of mobile phones in our life and their continuous transformation. From diagnosis to treatment planning, practice of diagnostically driven robotic assisted (DDRA) orthodontics all have improved to a great extent. 1974, marked the year for a technological boon in the field of dentistry. Orthodontics has been reinventing itself to... Digitalisation has been the talk of the town since the invent of mobile phones in our life and their continuous transformation. From diagnosis to treatment planning, practice of diagnostically driven robotic assisted (DDRA) orthodontics all have improved to a great extent. 1974, marked the year for a technological boon in the field of dentistry. Orthodontics has been reinventing itself to patient specific needs, evidence based care to establish new benchmarks. Virtual treatment planning is very common now-days along with digitally driven appliance manufacture using CAD/CAM techniques and 3D bioprinting. From Computed aided manufacture of hyrax devices, customized bracket production, soft tissue quantification in surgical cases to robotic wire bending, has led to the shift of the paradigm of orthodontics. Read More Read Less

Abstract Interval pattern concepts are a particular case of pattern structures. They can be used to clusterize rows of a numerical formal context (data matrix): two rows are close to each other if their entries at the corresponding positions fall within a given interval. The problem of mining interval pattern concepts has much in common with the known problem related to computational geometry: given a... Interval pattern concepts are a particular case of pattern structures. They can be used to clusterize rows of a numerical formal context (data matrix): two rows are close to each other if their entries at the corresponding positions fall within a given interval. The problem of mining interval pattern concepts has much in common with the known problem related to computational geometry: given a nite set of points in the Euclidean space, position a box of a given size in such a way that it encloses as many points as possible. This problem and its variations have been thoroughly studied in the case of a plane; however, the authors are not aware of the existence of algorithms which in a reasonable time produce an exact solution in the space of an arbitrary dimension. There exists an approximate greedy algorithm for solving this problem. It produces a solution with time which is linear in the number of points and polynomial in dimension. We apply a clustering approach based on that algorithm to the gene expression table from the dataset “The Cancer Cell Line Encyclopedia”. The resulting partition well agrees with a priori known biological factors. Read More Read Less