SQLite RDBMS Extension for Data Indexing Using B-tree Modifications
Multiway trees are one of the most popular solutions for the big data indexing. The most commonly used kind of the multiway trees is the B-tree. There exist different modifications of the B-trees, including B+-trees, B*-trees and B*+-trees considered in this work. However, these modifications are not supported by the popular open-source relational DBMS SQLite. This work is based on the previous research on the performance of multiway trees in the problem of structured data indexing, with the previously developed multiway trees C++ library usage. In this research the B*+-tree was developed as the data structure which combines the main B+-tree and B*-tree features together. Also, in the research the empirical computational complexities of different operations on the B-tree and its modifications were measured as well as the memory usage. The purpose of the current work is the development of the SQLite RDBMS extension which allows to use B-tree modifications (B+-tree, B*-tree and B*+-tree) as index structures in the SQLite RDBMS. The modifications of the base data structure were developed as a C++ library. The library is connected to the SQLite using the C-C++ cross-language API which is developed in the current work. The SQLite extension implements the novel algorithm for selecting the index structure (one of B-tree’s modifications) for some table of a database. The provided SQLite extension is adopted by the SQLiteEventLog component of the LDOPA process mining library. In addition, the experiment on the counting the empirical computational complexities of operations on the trees of different types is conducted using the developed in this work SQLite extension.
The article deals with the choice of the DBMS on the basis of application of decision support methods. It is shown that this task is complex multiobjective in nature. The technique is proposed for choosing the most rational DBMS in accordance with the priorities of the decision makers. The technique involves consistent application of calculating methods of the criteria weights on the basis of the rank method, the weighted estimates of the alternative DBMS calculating based on expert estimations clustering methods and analytical hierarchies method to calculate the final values of the alternatives. This technique will improve the objectivity of decision-making and automate the process of selecting a DBMS for corporate information systems.
When working with relational databases, the main time is loading, searching, update and unload data. When the amount of data is increased, the time to perform these operations is significantly increased, since in fact, all available records, and this reduces the performance and processing speed of the data. One possible way to increase productivity and increase speed data processing can be the use of indexes.
Earth remote sensing has always been a source of “big” data. Satellite data have inspired the development of “array” DBMS. An array DBMS processes N-dimensional (N-d) arrays utilizing a declarative query style to simplify raster data management and processing. However, raster data are traditionally stored in files, not in databases. Respective command line tools have long been developed to process these files. Most tools are feature-rich and free but optimized for a single machine. The approach of partially delegating in situ raster data processing to such tools has been recently proposed. The approach includes a new formal N-d array data model to abstract from the files and the tools as well as new distributed algorithms based on the model. This paper extends the approach with a new algorithm for the reshaping (tiling) of N-d arrays. The algorithm physically reorganizes the storage layout of N-d arrays to obtain an order of magnitude speedup. The extended approach outperforms SciDB up to 28× on retrospective Landsat data – one of the most typical and popular kind of satellite imagery. SciDB is the only freely available distributed array DBMS to date. Experiments were carried out on an 8-node cluster in Microsoft Azure Cloud.
In this paper, we consider a solution that helps increase the search speed and data fetching in relational databases such as Oracle and MySQL. This solution is called an index. We consider types of indices, which are unique only for specific DBMS, and indexes, which are used in almost all databases. Created by test database for experiments. The analysis is carried out on certain types of queries that are the same for all investigated DBMS. Based results of the work of the requests, a number of recommendations on the use of indices in specific DBMS and for specific types of query, as well as a number of common recommendations for writing relational queries.
In this work we consider a solution that helps to increase the speed of search and retrieval of data in relational database management systems, such as ORACLE and MySQL.
This solution is called the index. We consider the types of indexes that are unique only for a particular database and indexes that are used in all DBMS.
The test database for experiments is created. The analysis is performed on certain types of queries, the same for all test databases. Based on the results of queries made a number of recommendations for using specific indexes in data-bases for specific types of request, as well as some general advice on writing relational queries
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The geographic information system (GIS) is based on the first and only Russian Imperial Census of 1897 and the First All-Union Census of the Soviet Union of 1926. The GIS features vector data (shapefiles) of allprovinces of the two states. For the 1897 census, there is information about linguistic, religious, and social estate groups. The part based on the 1926 census features nationality. Both shapefiles include information on gender, rural and urban population. The GIS allows for producing any necessary maps for individual studies of the period which require the administrative boundaries and demographic information.
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.