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The diploma thesis deals with creation of computer aplication that can be used for conversion of raster maps for orienteering into model. Aplication will use this model to calculate optimal track between two selected locations. The thesis describes the development of a module for speed analysis in different types of terrain and another module for detection of roads and impassable objects. Thesis builds on work of Ing. Pavol Kršák and describes new features that were implemented to simplify user's control.

This term project focuses on algorithms for pattern matching used in modern IDS. The main focus is on regular expression matching. It deals with methods based on deterministic and nondeterministic finite automata, hybrid methods and with method based on regular expressions as programing langue for specialised processors. Implementation of pattern matching units based on some of described methodologies is described in next part. Methodology for resource consumption estimation is also described. Developed software system for unit generation is described in the next part. In the final part results are presented and discused.

The teasis deals with the creation of software tools, which use human element to extracts data necessary for navigation in the field of raster maps for orienteering. To achieve this goal, it is important to segment individual sections of the map according to symbols on the map. These symbols are illustrated on the map with one colour or combinations of colours, which create patterns. Because of identification of individual symbols, the work deals with segmentation according to colours and pattern matching. To navigate in the field is important information on height differences on the ground , for this reason the work discusses the position of contour identification and determination of their heights. Combining terrain data with data about height differences will create a 3D color models in XNA. To navigate in such a terrain that is used A* algorithm , in which the test several heuristic model defined distance. The A* algorithm then provides the optimum route according to specified criteria .The resulting application conveys the necessary tools to analyze raster maps orienteering , these tools are the manual and the automatic nature. The work includes detailed operating instructions of developed application.

The diploma thesis deals with creation of computer aplication that can be used for conversion of raster maps for orienteering into model. Aplication will use this model to calculate optimal track between two selected locations. The thesis describes the development of a module for speed analysis in different types of terrain and another module for detection of roads and impassable objects. Thesis builds on work of Ing. Pavol Kršák and describes new features that were implemented to simplify user's control.

This thesis consists of two parts. In part I, a group of combinatorial problems pertaining to strings, boolean matrices and graphs is studied. For given two strings x and y, their edit distance is the minimum number of character insertions, deletions and substitutions required to convert x into y. In this thesis we provide an algorithm that computes a constant approximation of edit distance in truly sub-quadratic time. Based on the provided ideas, we construct a separate sub- quadratic time algorithm that can find an occurrence of a pattern P in a given text T while allowing a few edit errors. Afterwards we study the boolean matrix multiplication (BMM) problem where given two boolean matrices, the aim is to find their product over boolean semi-ring. For this problem, we present two combinatorial models and show in these models BMM requires Ω(n3 /2O( √ log n) ) and Ω(n7/3 /2O( √ log n) ) work respectively. Furthermore, we also give a construction of a sparse sub-graph that preserves the distance between a designated source and any other vertex as long as the total weight increment of all the edges is bounded by some constant. In part II, we study the efficient construction of quasi-Gray codes. We give a construction of space optimal quasi-Gray codes over odd sized alphabets with read complexity 4...

Graph-structured data can be found in nearly every aspect of today's world, be it road networks, social networks or the internet itself. From a processing perspective, finding comprehensive patterns in graph-structured data is a core processing primitive in a variety of applications, such as fraud detection, biological engineering or social graph analytics. On the hardware side, multiprocessor systems, that consist of multiple processors in a single scale-up server, are the next important wave on top of multi-core systems. In particular, symmetric multiprocessor systems (SMP) are characterized by the fact, that each processor has the same architecture, e.g. every processor is a multi-core and all multiprocessors share a common and huge main memory space. Moreover, large SMPs will feature a non-uniform memory access (NUMA), whose impact on the design of efficient data processing concepts should not be neglected. The efficient usage of SMP systems, that still increase in size, is an interesting and ongoing research topic. Current state-of-the-art architectural design principles provide different and in parts disjunct suggestions on which data should be partitioned and or how intra-process communication should be realized. In this thesis, we propose a new synthesis of four of the most well-known principles Shared Everything, Partition Serial Execution, Data Oriented Architecture and Delegation, to create the NORAD architecture, which stands for NUMA-aware DORA with Delegation. We built our research prototype called NeMeSys on top of the NORAD architecture to fully exploit the provided hardware capacities of SMPs for graph pattern matching. Being an in-memory engine, NeMeSys allows for online data ingestion as well as online query generation and processing through a terminal based user interface. Storing a graph on a NUMA system inherently requires data partitioning to cope with the mentioned NUMA effect. Hence, we need to dissect the graph into a disjunct set of partitions, which can then be stored on the individual memory domains. This thesis analyzes the capabilites of the NORAD architecture, to perform scalable graph pattern matching on SMP systems. To increase the systems performance, we further develop, integrate and evaluate suitable optimization techniques. That is, we investigate the influence of the inherent data partitioning, the interplay of messaging with and without sufficient locality information and the actual partition placement on any NUMA socket in the system. To underline the applicability of our approach, we evaluate NeMeSys against synthetic datasets and perform an end-to-end evaluation of the whole system stack on the real world knowledge graph of Wikidata.

High-throughput next-generation sequencing techniques have hugely decreased the cost and increased the speed of sequencing, resulting in an explosion of sequencing data. This motivates the development of high-efficiency sequence alignment algorithms. In this thesis, I present multiple bit-parallel and Single Instruction Multiple Data (SIMD) algorithms that greatly accelerate the processing of biological sequences. The first chapter describes the BitPAl bit-parallel algorithms for global alignment with general integer scoring, which assigns integer weights for match, mismatch, and insertion/deletion. The bit-parallel approach represents individual cells in an alignment scoring matrix as bits in computer words and emulates the calculation of scores by a series of logic operations. Bit-parallelism has previously been applied to other pattern matching problems, producing fast algorithms. In timed tests, we show that BitPAl runs 7 - 25 times faster than a standard iterative algorithm. The second part involves two approaches to alignment with substitution scoring, which assigns a potentially different substitution weight to every pair of alphabet characters, better representing the relative rates of different mutations. The first approach extends the existing BitPAl method. The second approach is a new SIMD algorithm that uses partial sums of adjacent score differences. I present a simple partial sum method as well as one that uses parallel scan for additional acceleration. Results demonstrate that these algorithms are significantly faster than existing SIMD dynamic programming algorithms. Finally, I describe two extensions to the partial sums algorithm. The first adds support for affine gap penalty scoring. Affine gap scoring represents the biological likelihood that it is more likely for gaps to be continuous than to be distributed throughout a region by introducing a gap opening penalty and a gap extension penalty. The second extension is an algorithm that uses the partial sums method to calculate the tandem alignment of a pattern against a text sequence using a single pattern copy. Next generation sequencing data provides a wealth of information to researchers. Extracting that information in a timely manner increases the utility and practicality of sequence analysis algorithms. This thesis presents a family of algorithms which provide alignment scores in less time than previous algorithms.

Dynamic growth of computer networks encourages rapid development of network applications and services. To provide sufficient network service quality, it is important to limit some network flows based on their application protocol type. This thesis deals with the methods of network protocol identification and discusses their accuracy and suitability for multigigabit networks.  Based on the analysis, a protocol identification model was created and evaluated. The model was used for the design of hardware architecture accelerating computationally intensive operations of protocol identification.  The proposed solution is able to work on 10 Gb/s networks and export protocol information using NetFlow protocol.

The World-Wide Web contains a lot of information and reading through the web pages to collect this information is tedious, time consuming and error prone. Users need an automated solution for extracting or highlighting the data that they are interested in. Building a regular expression to match the text they are interested in will automate the process, but regular expressions are hard to create and certainly are not feasible for non-programmers to construct. Text Identification by Example (TIBE) makes it easier for end-users to harvest information from the web and other text documents. With TIBE, training text classifiers from user-selected positive and negative examples replaces the hand-writing of regular expressions. The text classifiers can then be used to extract or highlight text on web pages.