Michal Kratky

Reliability analysis of distribution networks

Monitoring of failures and outages in the transmission and distribution of electrical energy is necessary for determination of the reliability of network components and the supply of electrical energy to consumers. Incorrect input data leads, of course, to false results even if the correct computing method is used. The paper deals with obtaining of reliability indices of distribution network analyzing data about failures and outages of electric power supply from several distribution areas from the Czech Republic and the Slovak Republic. Data sources produce heterogeneous domain data. It is necessary to store them in one common scheme to be able to analyze and query such data. Scheme comes from Distribution Network Grid Code. We proposed multi-dimensional data structure to storage of the data that enable effective querying. Data from particular companies are transformed. The transformation language has been developed for this reason. For evaluation of reliability it is necessary to have data on the number and range of the examined piece of equipment. Thus it is not only the case of data on e.g. the number of failures and the mean duration of a failure of the line of the given type and the given voltage level, but also the case of data on the total range of the observed piece of equipment, i.e. here on the total length of the line of the given type and the given voltage level. The result of analysis is the determination of the failure rate and mean failure duration for particular items of equipment or for group of equipments. With more detailed databases, other pieces of information may be found that are important for operators, such as the most frequent cause of failures, areas of the greatest amounts of undelivered energy, etc.

Information Extraction from HTML Product Catalogues: From Source Code and Images to RDF

We describe an application of information extraction from company Web sites focusing on product offers. A statistical approach to text analysis is used in conjunction with different ways of image classification. Ontological knowledge is used to group the extracted items into structured objects. The results are stored in an RDF repository and made available for structured search.

Efficient Processing of Narrow Range Queries in Multi-dimensional Data Structures

Multi-dimensional data structures are applied in many real index applications, i.e. data mining, indexing multimedia data, indexing of text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing: data structures to indexing metric and vector spaces. R-trees, R*-trees and (B)UB-trees are representatives of the vector data structures. These data structures provide efficient processing of many types of queries, i.e. point queries, range queries and so on. As far as the vector data structures are concerned, the range query retrieves all points in defined hyper box in an n-dimensional space. The narrow range query is an important type of the range query. Its processing is inefficient in vector data structures. Moreover, the efficiency decreases as the dimension of the indexed space increases. We depict an application of the signature for more efficient processing of narrow range queries. The approach puts the signature into the multi-dimensional data structures like R-tree or UB-tree but original functionalities are preserved, i.e. the range query algorithm for general range query. The novel data structure is called the signature data structure, e.g., signature R-tree or signature UB-tree

Index-based n-gram extraction from large document collections

N-grams are applied in some applications searching in text documents, especially in cases when one must work with phrases, e.g. in plagiarism detection. N-gram is a sequence of n terms (or generally tokens) from a document. We get a set of n-grams by moving a floating window from the begin to the end of the document. During the extraction we must remove duplicate n-grams and we must store additional values to each n-gram type, e.g. n-gram type frequency for each document and so on, it depends on a query model used. Previous works utilize a sorting algorithm to compute the n-gram frequency. These approaches must handle a high number of the same n-grams resulting in high time and space overhead. Moreover, these techniques are often main-memory only, it means they must be executed for small or middle size collections. In this paper, we show an index-based method to the n-gram extraction for large collections. This method utilizes common data structures like B+-tree and Hash table. We show the scalability of our method by presenting experiments with the gigabytes collection.

On support of ordering in multidimensional data structures

Multidimensional data structures are applied in many areas, e.g. in data mining, indexing multimedia data and text documents, and so on. There are some applications where the range query result must be ordered. A typical case is the result with tuples sorted according to values in one dimension defined by the ORDER BY clause of an SQL statement. If we use a multidimensional data structure, the result set is sorted after the range query is processed. Since the sort operation must often be processed on tuples stored in the secondary storage, an external sorting algorithm must be utilized. Therefore, this operation is time consuming especially for a large result set. In this paper, we introduce a new data structure, a variant of the R-tree, supporting a storage of ordered tuples.

Reliability analysis of electric distribution system

The paper deals with the evaluation of distribution system reliability parameters. Knowledge of the component reliability parameters in power networks is necessary for the reliability analyses and also for maintenance optimization systems. Component reliability parameters are possible to retrieve only with accurate databases of distribution system operators. Such a database includes records of outages and interruptions in electrical systems. Quite a big problem for a querying of these databases is that databases of various operators differ from one another. It is impossible to get reliability parameters from this data without preprocessing. This paper describes a framework which enables the retrieval of parameters from various databases. There are also actual results.