The Karst Feature Database (KFD) of Minnesota is a relational GIS-based Database Management System (DBMS). Previous karst feature datasets used inconsistent attributes to describe karst features in different areas of Minnesota. Existing metadata were modified and standardized to represent a comprehensive metadata for all the karst features in Minnesota. Microsoft Access 2000 and ArcView 3.2 were used to develop this working database. Existing county and sub-county karst feature datasets have been assembled into the KFD, which is capable of visualizing and analyzing the entire data set. By November 17 2002, 11,682 karst features were stored in the KFD of Minnesota. Data tables are stored in a Microsoft Access 2000 DBMS and linked to corresponding ArcView applications. The current KFD of Minnesota has been moved from a Windows NT server to a Windows 2000 Citrix server accessible to researchers and planners through networked interfaces.

Database systems build an integral component of today’s software systems and as such they are the central point for storing and sharing a software system’s data while ensuring global data consistency at the same time. Introducing the primitives of roles and their accompanied metatype distinction in modeling and programming languages, results in a novel paradigm of designing, extending, and programming modern software systems. In detail, roles as modeling concept enable a separation of concerns within an entity. Along with its rigid core, an entity may acquire various roles in different contexts during its lifetime and thus, adapts its behavior and structure dynamically during runtime. Unfortunately, database systems, as important component and global consistency provider of such systems, do not keep pace with this trend. The absence of a metatype distinction, in terms of an entity’s separation of concerns, in the database system results in various problems for the software system in general, for the application developers, and finally for the database system itself. In case of relational database systems, these problems are concentrated under the term role-relational impedance mismatch. In particular, the whole software system is designed by using different semantics on various layers. In case of role-based software systems in combination with relational database systems this gap in semantics between applications and the database system increases dramatically. Consequently, the database system cannot directly represent the richer semantics of roles as well as the accompanied consistency constraints. These constraints have to be ensured by the applications and the database system loses its single point of truth characteristic in the software system. As the applications are in charge of guaranteeing global consistency, their development requires more effort in data management. Moreover, the software system’s data management is distributed over several layers, which results in an unstructured software system architecture. To overcome the role-relational impedance mismatch and bring the database system back in its rightful position as single point of truth in a software system, this thesis introduces the novel and tripartite RSQL approach. It combines a novel database model that represents the metatype distinction as first class citizen in a database system, an adapted query language on the database model’s basis, and finally a proper result representation. Precisely, RSQL’s logical database model introduces Dynamic Data Types, to directly represent the separation of concerns within an entity type on the schema level. On the instance level, the database model defines the notion of a Dynamic Tuple that combines an entity with the notion of roles and thus, allows for dynamic structure adaptations during runtime without changing an entity’s overall type. These definitions build the main data structures on which the database system operates. Moreover, formal operators connecting the query language statements with the database model data structures, complete the database model. The query language, as external database system interface, features an individual data definition, data manipulation, and data query language. Their statements directly represent the metatype distinction to address Dynamic Data Types and Dynamic Tuples, respectively. As a consequence of the novel data structures, the query processing of Dynamic Tuples is completely redesigned. As last piece for a complete database integration of a role-based notion and its accompanied metatype distinction, we specify the RSQL Result Net as result representation. It provides a novel result structure and features functionalities to navigate through query results. Finally, we evaluate all three RSQL components in comparison to a relational database system. This assessment clearly demonstrates the benefits of the roles concept’s full database integration.

Linear algebra operations appear in nearly every application in advanced analytics, machine learning, and of various science domains. Until today, many data analysts and scientists tend to use statistics software packages or hand-crafted solutions for their analysis. In the era of data deluge, however, the external statistics packages and custom analysis programs that often run on single-workstations are incapable to keep up with the vast increase in data volume and size. In particular, there is an increasing demand of scientists for large scale data manipulation, orchestration, and advanced data management capabilities. These are among the key features of a mature relational database management system (DBMS). With the rise of main memory database systems, it now has become feasible to also consider applications that built up on linear algebra. This thesis presents a deep integration of linear algebra functionality into an in-memory column-oriented database system. In particular, this work shows that it has become feasible to execute linear algebra queries on large data sets directly in a DBMS-integrated engine (LAPEG), without the need of transferring data and being restricted by hard disc latencies. From various application examples that are cited in this work, we deduce a number of requirements that are relevant for a database system that includes linear algebra functionality. Beside the deep integration of matrices and numerical algorithms, these include optimization of expressions, transparent matrix handling, scalability and data-parallelism, and data manipulation capabilities. These requirements are addressed by our linear algebra engine. In particular, the core contributions of this thesis are: firstly, we show that the columnar storage layer of an in-memory DBMS yields an easy adoption of efficient sparse matrix data types and algorithms. Furthermore, we show that the execution of linear algebra expressions significantly benefits from different techniques that are inspired from database technology. In a novel way, we implemented several of these optimization strategies in LAPEG’s optimizer (SpMachO), which uses an advanced density estimation method (SpProdest) to predict the matrix density of intermediate results. Moreover, we present an adaptive matrix data type AT Matrix to obviate the need of scientists for selecting appropriate matrix representations. The tiled substructure of AT Matrix is exploited by our matrix multiplication to saturate the different sockets of a multicore main-memory platform, reaching up to a speed-up of 6x compared to alternative approaches. Finally, a major part of this thesis is devoted to the topic of data manipulation; where we propose a matrix manipulation API and present different mutable matrix types to enable fast insertions and deletes. We finally conclude that our linear algebra engine is well-suited to process dynamic, large matrix workloads in an optimized way. In particular, the DBMS-integrated LAPEG is filling the linear algebra gap, and makes columnar in-memory DBMS attractive as efficient, scalable ad-hoc analysis platform for scientists.

Linear algebra operations appear in nearly every application in advanced analytics, machine learning, and of various science domains. Until today, many data analysts and scientists tend to use statistics software packages or hand-crafted solutions for their analysis. In the era of data deluge, however, the external statistics packages and custom analysis programs that often run on single-workstations are incapable to keep up with the vast increase in data volume and size. In particular, there is an increasing demand of scientists for large scale data manipulation, orchestration, and advanced data management capabilities. These are among the key features of a mature relational database management system (DBMS). With the rise of main memory database systems, it now has become feasible to also consider applications that built up on linear algebra. This thesis presents a deep integration of linear algebra functionality into an in-memory column-oriented database system. In particular, this work shows that it has become feasible to execute linear algebra queries on large data sets directly in a DBMS-integrated engine (LAPEG), without the need of transferring data and being restricted by hard disc latencies. From various application examples that are cited in this work, we deduce a number of requirements that are relevant for a database system that includes linear algebra functionality. Beside the deep integration of matrices and numerical algorithms, these include optimization of expressions, transparent matrix handling, scalability and data-parallelism, and data manipulation capabilities. These requirements are addressed by our linear algebra engine. In particular, the core contributions of this thesis are: firstly, we show that the columnar storage layer of an in-memory DBMS yields an easy adoption of efficient sparse matrix data types and algorithms. Furthermore, we show that the execution of linear algebra expressions significantly benefits from different techniques that are inspired from database technology. In a novel way, we implemented several of these optimization strategies in LAPEG’s optimizer (SpMachO), which uses an advanced density estimation method (SpProdest) to predict the matrix density of intermediate results. Moreover, we present an adaptive matrix data type AT Matrix to obviate the need of scientists for selecting appropriate matrix representations. The tiled substructure of AT Matrix is exploited by our matrix multiplication to saturate the different sockets of a multicore main-memory platform, reaching up to a speed-up of 6x compared to alternative approaches. Finally, a major part of this thesis is devoted to the topic of data manipulation; where we propose a matrix manipulation API and present different mutable matrix types to enable fast insertions and deletes. We finally conclude that our linear algebra engine is well-suited to process dynamic, large matrix workloads in an optimized way. In particular, the DBMS-integrated LAPEG is filling the linear algebra gap, and makes columnar in-memory DBMS attractive as efficient, scalable ad-hoc analysis platform for scientists.

Database systems build an integral component of today’s software systems and as such they are the central point for storing and sharing a software system’s data while ensuring global data consistency at the same time. Introducing the primitives of roles and their accompanied metatype distinction in modeling and programming languages, results in a novel paradigm of designing, extending, and programming modern software systems. In detail, roles as modeling concept enable a separation of concerns within an entity. Along with its rigid core, an entity may acquire various roles in different contexts during its lifetime and thus, adapts its behavior and structure dynamically during runtime. Unfortunately, database systems, as important component and global consistency provider of such systems, do not keep pace with this trend. The absence of a metatype distinction, in terms of an entity’s separation of concerns, in the database system results in various problems for the software system in general, for the application developers, and finally for the database system itself. In case of relational database systems, these problems are concentrated under the term role-relational impedance mismatch. In particular, the whole software system is designed by using different semantics on various layers. In case of role-based software systems in combination with relational database systems this gap in semantics between applications and the database system increases dramatically. Consequently, the database system cannot directly represent the richer semantics of roles as well as the accompanied consistency constraints. These constraints have to be ensured by the applications and the database system loses its single point of truth characteristic in the software system. As the applications are in charge of guaranteeing global consistency, their development requires more effort in data management. Moreover, the software system’s data management is distributed over several layers, which results in an unstructured software system architecture. To overcome the role-relational impedance mismatch and bring the database system back in its rightful position as single point of truth in a software system, this thesis introduces the novel and tripartite RSQL approach. It combines a novel database model that represents the metatype distinction as first class citizen in a database system, an adapted query language on the database model’s basis, and finally a proper result representation. Precisely, RSQL’s logical database model introduces Dynamic Data Types, to directly represent the separation of concerns within an entity type on the schema level. On the instance level, the database model defines the notion of a Dynamic Tuple that combines an entity with the notion of roles and thus, allows for dynamic structure adaptations during runtime without changing an entity’s overall type. These definitions build the main data structures on which the database system operates. Moreover, formal operators connecting the query language statements with the database model data structures, complete the database model. The query language, as external database system interface, features an individual data definition, data manipulation, and data query language. Their statements directly represent the metatype distinction to address Dynamic Data Types and Dynamic Tuples, respectively. As a consequence of the novel data structures, the query processing of Dynamic Tuples is completely redesigned. As last piece for a complete database integration of a role-based notion and its accompanied metatype distinction, we specify the RSQL Result Net as result representation. It provides a novel result structure and features functionalities to navigate through query results. Finally, we evaluate all three RSQL components in comparison to a relational database system. This assessment clearly demonstrates the benefits of the roles concept’s full database integration.

Atliekant duomenų bazės projektavimo darbus visuomet yra svarbu žinoti ar pasirinkta sistema veiks taip kaip tikimasi. Šio darbo tikslas sukurti SQL serverių testavimo įrankį, kuris padės renkantis vienokią ar kitokią sistemą. Darbe išanalizuoti panašūs projektai, įvertinti tų projektų privalumai bei trūkumai. / Making a data base project always important to know - do the system will work properly. The main point of this job is to create the test system of SQL servers (or DBMS – Data Bases Management System) which will help to chose that or either machine configuration. In this work were analyzed the similar projects, appraised the necessity and imperfections of this projects.

Šiame darbe apžvelgiama esamų GIS sprendimų privalumai ir trūkumai, tiriamos skaitmeninių žemėlapių įmonių taikomosiose programose panaudojimo galimybės. Analizuojama Oracle DBVS programinė įranga, siekiant įrodyti, kad ji turi reikiamas funkcijas erdvinių duomenų valdymui. Apibendrinus analizės rezultatus, pasiūlomas naujas GIS sprandimas, kuris leidžia atvaizduoti įmonės aprašomuosius duomenis skaitmeniniame žemėlapyje dinamišku būdu, nežinant jų struktūros ir minimizuojant programavimo darbus. Dinamiškos GIS koncepcijai realizuoti suprojektuojama nauja dinamiškos GIS architektūra ir suprogramuojamas trūkstamas duomenų integravimo komponentas. Dinamiškam duomenų susiejimui užtikrinti, duomenų integravimo komponento veikimas pagrindžiamas veiklos taisyklių koncepcija. Dinamiškam erdvinių ir aprašomųjų duomenų susiejimui pademonstruoti, dinamiškos GIS architektūra realizuojama naudojant oracle programinę įrangą, duomenų integravimo komponentas sukonfigūruojamas mapViewer naudojimui. Dinamiškos GIS testavimui sukurta testavimo sąsaja, kuri leidžia tiesiai internetiniame puslapyje rašyti veiklos taisykles ir matyti rezutatus žemėlapyje. Į oracle duomenų bazę importuoti egzistuojsntys erdviniai duomenys ir testavimo tikslais dalis duomenų buvo sukurti rankiniu būdu. / This work reviews advantages and weaknesses of existing GIS solutions, explores usage of digital maps for rendering specific enterprise data in digital maps. Oracle DBMS software was also analyzed to prove that it has all necessary components for spatial data manipulation. After summarizing analysis results we offer new GIS solution, which allows rendering enterprise data on the map in dynamic way without knowing enterprise data structure and minimizing programmer work. To implement a dynamic GIS conception a new dynamic GIS architecture and the missing component design for representing business data in a map are created. To ensure dynamic integration and simple usage of component, dynamic integration component functionality is based on business rules conception. To demonstrate dynamic enterprise and spatial data integration on the map, dynamic GIS architecture was implemented using Oracle software by configuring data integration component to use Oracle mapViewer. For dynamic GIS testing, test interface was created with an ability to write business rules directly in web page and see the integration results. Existing spatial data was imported into Oracle DB and some spatial data for testing purposes was created manually.

Duomenų bazių valdymo sistema (DBVS) yra pagrindas beveik visų šiuolaikinių informacinių sistemų (IS). Iš esmės kiekvienas verslo, mokslo arba valdžios valdymo procesas remiasi duomenų baze. Interneto plėtra tik paspartino šią tendenciją – šiandien duomenų bazių operacijos yra kiekvieno duomenų pakeitimo didesniuose tinklalapiuose, paieškos arba apsipirkimo internete variklis [1]. Šiuo metu rinkoje yra didelis komercinių ir nemokamų (taip pat ir atviro kodo) duomenų bazių valdymo sistemų (DBVS) pasirinkimas, pavyzdžiui: Oracle, Microsoft SQL Server, IBM DB2, Microsoft Access, MySQL, PostgreSQL. Kiekviena jų turi savo privalumų ir trukumų. Tačiau informacinių sistemų projektavimo eiga, naudojant šias DBVS ir neatsižvelgiant į jų ypatumus, yra panaši: suprojektuojama duomenų bazė (sukuriamos lentelės, nustatomi jų tarpusavio ryšiai), rašomos užklausos, kuriamos (arba generuojamos) duomenų įvedimo/redagavimo formos bei kuriamos duomenų išrinkimo ataskaitos. Ši informacinių sistemų kūrimo tvarka yra nusistovėjusi per daugelį metų. Tačiau DB projektavimo procesas taptų lengvesnis, pakeitus IS projektavimo procesą taip, kad realizacijos metu iš pradžių būtų kuriamos formos, o tik po to iš sukurtų formų būtų generuojama duomenų bazė. Toks IS kūrimo procesas leistų iš dalies automatizuotų DB projektavimą. Be to, galutinai suderinus prototipus su užsakovu, užtektų tik sugeneruoti DB, t.y. nereikėtų iš naujo kurti formų, o sistema sugeneruotų DB bei automatiškai susietų formų laukus... [toliau žr. visą tekstą] / The Data Base Management System (DBMS) is the foundation of almost every modern business information system. Virtually every administrative process in business, science or government relies on a database. There are a lot of DBMS products in our days, such as Oracle, Microsoft SQL Server, IBM DB2, Microsoft Access, MySQL and PostgreSQL. Each of it has their advantages and disadvantages. But the database design process using these DBMS is the same – at the first stage we need to create a database (tables and relationships between them), then we need to create (or generate by using wizard) forms for data input/modification and reports for data selection. However, the database design process would become easier by changing database design process in such a way that at first we create forms and then database is generated from forms data and forms are automatically associated with database tables. The task of research is to extend chosen free open source DBMS by new functions which would enable to develop forms and DB using new methods – automated database generation from forms and automatic forms association with database tables. OpenOffice.org Base DBMS and Java programming language has been chosen for the task implementation. This thesis consists of analysis, design, user manual, experimental and conclusion parts.

Energy consumption has become a first-class optimization goal in design and implementation of data-intensive computing systems. This is particularly true in the design of database management systems (DBMS), which is one of the most important servers in software stack of modern data centers. Data storage system is one of the essential components of database and has been under many research efforts aiming at reducing its energy consumption. In previous work, dynamic power management (DPM) techniques that make real-time decisions to transition the disks to low-power modes are normally used to save energy in storage systems. In this research, we tackle the limitations of DPM proposals in previous contributions and design a dynamic energy-aware disk storage system in database servers. We introduce a DPM optimization model integrated with model predictive control (MPC) strategy to minimize power consumption of the disk-based storage system while satisfying given performance requirements. It dynamically determines the state of disks and plans for inter-disk data fragment migration to achieve desirable balance between power consumption and query response time. Furthermore, via analyzing our optimization model to identify structural properties of optimal solutions, a fast-solution heuristic DPM algorithm is proposed that can be integrated in large-scale disk storage systems, where finding the most optimal solution might be long, to achieve near-optimal power saving solution within short periods of computational time. The proposed ideas are evaluated through running simulations using extensive set of synthetic workloads. The results show that our solution achieves up to 1.65 times more energy saving while providing up to 1.67 times shorter response time compared to the best existing algorithm in literature. Stream join is a dynamic and expensive database operation that performs join operation in real-time fashion on continuous data streams. Stream joins, also known as window joins, impose high computational time and potentially higher energy consumption compared to other database operations, and thus we also tackle energy-efficiency of stream join processing in this research. Given that there is a strong linear correlation between energy-efficiency and performance of in-memory parallel join algorithms in database servers, we study parallelization of stream join algorithms on multicore processors to achieve energy efficiency and high performance. Equi-join is the most frequent type of join in query workloads and symmetric hash join (SHJ) algorithm is the most effective algorithm to evaluate equi-joins in data streams. To best of our knowledge, we are the first to propose a shared-memory parallel symmetric hash join algorithm on multi-core CPUs. Furthermore, we introduce a novel parallel hash-based stream join algorithm called chunk-based pairing hash join that aims at elevating data throughput and scalability. We also tackle parallel processing of multi-way stream joins where there are more than two input data streams involved in the join operation. To best of our knowledge, we are also the first to propose an in-memory parallel multi-way hash-based stream join on multicore processors. Experimental evaluation on our proposed parallel algorithms demonstrates high throughput, significant scalability, and low latency while reducing the energy consumption. Our parallel symmetric hash join and chunk-based pairing hash join achieve up to 11 times and 12.5 times more throughput, respectively, compared to that of state-of-the-art parallel stream join algorithm. Also, these two algorithms provide up to around 22 times and 24.5 times more throughput, respectively, compared to that of non-parallel (sequential) stream join computation where there is one processing thread.

The world is becoming increasingly digitized, and with this trend comes an increas-ing need for storing data for companies of all sizes. For smaller enterprises, thiscould prove to be a major challenge due to limitations in knowledge and financialassets. So the purpose of this study is to investigate how smaller companies cansatisfy their needs for data storage and which database management system to usein order to not let their shortcomings hold their development and growth back. Tofulfill this purpose, a small wood processing company in Sweden is examined andused as an example. To investigate and answer the problem, literary research is con-ducted to gain knowledge about data storage and the different options for this thatexist. Microsoft Access, MySQL, and MongoDB are selected for evaluation andtheir performance is compared in controlled experiments. The results of this studyindicates that, due to the small amount of data that the example company possesses,the simplicity of Microsoft Access trumps the high performance of its competitors.However, with increasingly developed internet infrastructure, the option of hostinga database in the cloud has become a feasible option. If hosting the database in thecloud is the desired solution, Microsoft Access has a higher operating cost than theother alternatives, making MySQL come out on top.