Piotr Przymus

Recursive Query Facilities in Relational Databases: A Survey

The relational model is the basis for most modern databases, while SQL is the most commonly used query language. However, there are data structures and computational problems that cannot be expressed using SQL-92 queries. Among them are those concerned with the bill-of-material and corporate hierarchies. A newer standard, called the SQL-99, introduced recursive queries which can be used to solve such tasks. Yet, only recently recursive queries have been implemented in most of the leading relational databases. In this paper we have reviewed and compared implementations of the recursive queries defined by SQL:1999 through SQL:2008 and offered by leading vendors of DBMSs. Our comparison concerns features, syntax and performance.

Dynamic Compression Strategy for Time Series Database Using GPU

Nowadays, we can observe increasing interest in processing and exploration of time series. Growing volumes of data and needs of efficient processing pushed research in new directions. GPU devices combined with fast compression and decompression algorithms open new horizons for data intensive systems. In this paper we present improved cascaded compression mechanism for time series databases build on Big Table–like solution. We achieved extremely fast compression methods with good compression ratio.

Improving Efficiency of Data Intensive Applications on GPU Using Lightweight Compression

In many scientific and industrial applications GPGPU (General-Purpose Computing on Graphics Processing Units) programming reported excellent speed-up when compared to traditional CPU (central processing unit) based libraries. However, for data intensive applications this benefit may be much smaller or may completely disappear due to time consuming memory transfers. Up to now, gain from processing on the GPU was noticeable only for problems where data transfer could be compensated by calculations, which usually mean large data sets and complex computations. This paper evaluates a new method of data decompression directly in GPU shared memory which minimizes data transfers on the path from disk, through main memory, global GPU device memory, to GPU processor. The method is successfully applied to pattern matching problems. Results of experiments show considerable speed improvement for large and small data volumes which is a significant step forward in GPGPU computing.

Application of wavelets and kernel methods to detection and extraction of behaviours of freshwater mussels

Some species of mussels are well-known bioindicators and may be used to create a Biological Early Warning System. Such systems use long-term observations of mussels activity for monitoring purposes. Yet, many of these systems are based on statistical methods and do not use all the potential that stays behind the data derived from the observations. In the paper we propose an algorithm based on wavelets and kernel methods to detect behaviour events in the collected data. We present our algorithm together with a discussion on the influence of various parameters on the received results. The study describes obtaining and pre-processing raw data and a feature extraction algorithm. Other papers which applied mathematical apparatus to Biological Early Warning Systems used much simpler methods and their effectiveness was questionable. We verify the results using a system with prepared tags for specified events. This leads us to a classification of these events and creating a Dreissena polymorpha behaviour dictionary and a Biological Early Warning System. Results from preliminary experiments show, that such a formulation of the problem, allows extracting relevant information from a given signal and yields an effective solution of the considered problem.

Time Series Queries Processing with GPU Support

In recent years, an increased interest in processing and exploration of time-series has been observed. Due to the growing volumes of data, extensive studies have been conducted in order to find new and effective methods for storing and processing data. Research has been carried out in different directions, including hardware based solutions or NoSQL databases. We present a prototype query engine based on GPGPU and NoSQL database plus a new model of data storage using lightweight compression. Our solution improves the time series database performance in all aspects and after some modifications can be also extended to general-purpose databases in the future.

Improving High-Performance GPU Graph Traversal with Compression

Traversing huge graphs is a crucial part of many real-world problems, including graph databases. We show how to apply Fixed Length lightweight compression method for traversing graphs stored in the GPU global memory. This approach allows for a significant saving of memory space, improves data alignment, cache utilization and, in many cases, also processing speed. We tested our solution against the state-of-the-art implementation of BFS for GPU and obtained very promising results.

A Bi-objective Optimization Framework for Heterogeneous CPU/GPU Query Plans

Graphics Processing Units (GPU) have significantly more applications than just rendering images. They are also used in general-purpose computing to solve problems that can benefit from massive parallel processing. However, there are tasks that either hardly suit GPU or fit GPU only partially. The latter class is the focus of this paper. We elaborate on hybrid CPU/GPU computation and build optimization methods that seek the equilibrium between these two computation platforms. The method is based on heuristic search for bi-objective Pareto optimal execution plans in presence of multiple concurrent queries. The underlying model mimics the commodity market where devices are producers and queries are consumers. The value of resources of computing devices is controlled by supply-and-demand laws. Our model of the optimization criteria allows finding solutions of problems not yet addressed in heterogeneous query processing. Furthermore, it also offers lower time complexity and higher accuracy than other methods.

Compression Planner for Time Series Database with GPU Support

Nowadays, we can observe increasing interest in processing and exploration of time series. Growing volumes of data and needs of efficient processing pushed research in new directions. This paper presents a lossless lightweight compression planner intended to be used in a time series database system. We propose a novel compression method which is ultra fast and tries to find the best possible compression ratio by composing several lightweight algorithms tuned dynamically for incoming data. The preliminary results are promising and open new horizons for data intensive monitoring and analytic systems.

Zebra mussels' behaviour detection, extraction and classification using wavelets and kernel methods

This paper concerns the detection, feature extraction and classification of behaviours of Dreissena polymorpha. A new algorithm based on wavelets and kernel methods that detects relevant events in the collected data is presented. This algorithm allows us to extract elementary events from the behaviour of a living organism. Moreover, we propose an efficient framework for automatic classification to separate the control and stressful conditions.

Profile based recommendation of code reviewers

Code reviews consist in proof-reading proposed code changes in order to find their shortcomings such as bugs, insufficient test coverage or misused design patterns. Code reviews are conducted before merging submitted changes into the main development branch. The selection of suitable reviewers is crucial to obtain the high quality of reviews. In this article we present a new method of recommending reviewers for code changes. This method is based on profiles of individual programmers. For each developer we maintain his/her profile. It is the multiset of all file path segments from commits reviewed by him/her. It will get updated when he/she presents a new review. We employ a similarity function between such profiles and change proposals to be reviewed. The programmer whose profile matches the change most is recommended to become the reviewer. We performed an experimental comparison of our method against state-of-the-art techniques using four large open-source projects. We obtained improved results in terms of classification metrics (precision, recall and F-measure) and performance (we have lower time and space complexity).