Query processing in temporal object-oriented databases

This PhD thesis is concerned with historical data management in the context of objectoriented databases. An extensible approach has been explored to processing temporal object queries within a uniform query framework. By the uniform framework, we mean temporal queries can be processed within the existing object-oriented framework that is extended from relational framework, by extending the existing query processing techniques and strategies developed for OODBs and RDBs. The unified model of OODBs and RDBs in UmSQL/X has been adopted as a basis for this purpose. A temporal object data model is thereby defined by incorporating a time dimension into this unified model of OODBs and RDBs to form temporal relational-like cubes but with the addition of aggregation and inheritance hierarchies. A query algebra, that accesses objects through these associations of aggregation, inheritance and timereference, is then defined as a general query model /language. Due to the extensive features of our data model and reducibility of the algebra, a layered structure of query processor is presented that provides a uniforrn framework for processing temporal object queries. Within the uniform framework, query transformation is carried out based on a set of transformation rules identified that includes the known relational and object rules plus those pertaining to the time dimension. To evaluate a temporal query involving a path with timereference, a strategy of decomposition is proposed. That is, evaluation of an enhanced path, which is defined to extend a path with time-reference, is decomposed by initially dividing the path into two sub-paths: one containing the time-stamped class that can be optimized by making use of the ordering information of temporal data and another an ordinary sub-path (without time-stamped classes) which can be further decomposed and evaluated using different algorithms. The intermediate results of traversing the two sub-paths are then joined together to create the query output. Algorithms for processing the decomposed query components, i. e., time-related operation algorithms, four join algorithms (nested-loop forward join, sort-merge forward join, nested-loop reverse join and sort-merge reverse join) and their modifications, have been presented with cost analysis and implemented with stream processing techniques using C++. Simulation results are also provided. Both cost analysis and simulation show the effects of time on the query processing algorithms: the join time cost is linearly increased with the expansion in the number of time-epochs (time-dimension in the case of a regular TS). It is also shown that using heuristics that make use of time information can lead to a significant time cost saving. Query processing with incomplete temporal data has also been discussed

mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Дослідження та вдосконалення алгоритмів адміністрування мережевої бази даних “Navi”

Робота публікується згідно наказу ректора від 29.12.2020 р. №580/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії НАУ". Керівник проекту: к.т.н., доцент Проценко Микола МихайловичLong before the advent of computerized databases, humanity already then had a need to store information in a structured form. Since at a time when computers either did not exist at all, or they were just entering the market and were at the stage of their formation, peculiar databases existed in written or physical form, for example, data archives, reference centers, libraries, ledgers, telephone directories etc. And since now there is a need to store huge amounts of information, while taking up as little storage space and resources as possible, databases are an integral stage in the development of opportunities to simplify the life of mankind. The database is both a tool and the very subject of a collection of data, which shows the state of certain objects and their relationship in a certain subject area.Задовго до появи комп'ютеризованих баз даних людство вже тоді мало потребу зберігати інформацію у структурованому вигляді. Оскільки в той час, коли комп’ютери або взагалі не існували, або вони тільки виходили на ринок і були на стадії свого формування, своєрідні бази даних існували в письмовій або фізичній формі, наприклад, архіви даних, довідкові центри, бібліотеки, книги , телефонні довідники тощо. І оскільки зараз існує потреба зберігати величезні обсяги інформації, займаючи при цьому якомога менше місця для зберігання та ресурсів, бази даних є невід’ємним етапом у розвитку можливостей спрощення життя людства. База даних є одночасно інструментом і самим предметом збору даних, який показує стан певних об’єктів та їх взаємозв’язок у певній предметній області

A rapid prototyping/artificial intelligence approach to space station-era information management and access

Applications of rapid prototyping and Artificial Intelligence techniques to problems associated with Space Station-era information management systems are described. In particular, the work is centered on issues related to: (1) intelligent man-machine interfaces applied to scientific data user support, and (2) the requirement that intelligent information management systems (IIMS) be able to efficiently process metadata updates concerning types of data handled. The advanced IIMS represents functional capabilities driven almost entirely by the needs of potential users. Space Station-era scientific data projected to be generated is likely to be significantly greater than data currently processed and analyzed. Information about scientific data must be presented clearly, concisely, and with support features to allow users at all levels of expertise efficient and cost-effective data access. Additionally, mechanisms for allowing more efficient IIMS metadata update processes must be addressed. The work reported covers the following IIMS design aspects: IIMS data and metadata modeling, including the automatic updating of IIMS-contained metadata, IIMS user-system interface considerations, including significant problems associated with remote access, user profiles, and on-line tutorial capabilities, and development of an IIMS query and browse facility, including the capability to deal with spatial information. A working prototype has been developed and is being enhanced

Modellgetriebene Entwicklung inhaltsbasierter Bildretrieval-Systeme auf der Basis von objektrelationalen Datenbank-Management-Systeme

In this thesis, the model-driven software development paradigm is employed in order to support the development of Content-based Image Retrieval Systems (CBIRS) for different application domains. Modeling techniques, based on an adaptable conceptual framework model, are proposed for deriving the components of a concrete CBIRS. Transformation techniques are defined to automatically implement the derived application specific models in an object-relational database management system. A set of criteria assuring the quality of the transformation are derived from the theory for preserving information capacity applied in database design.In dieser Dissertation wird das Paradigma des modellgetriebenen Softwareentwurfs für die Erstellung von inhaltsbasierten Bildretrieval-Systemen verwendet. Ein adaptierbares Frameworkmodell wird für die Ableitung des Modells eines konkreten Bildretrieval-Systems eingesetzt. Transformationstechniken für die automatische Generierung von Implementierungen in Objektorientierten Datenbank-Management-Systemen aus dem konzeptuellen Modell werden erarbeitet. Die aus der Theorie des Datenbankentwurfs bekannten Anforderungen zur Kapazitätserhaltung der Transformation werden verwendet, um Kriterien für die erforderliche Qualität der Transformation zu definieren

Exploring graph databases and possible benefits of utilization in Content Services Platforms : Case: M-Files

Interest in graph database technology has been raised by successful implementations of proprietary graph databases such as used by Twitter and Facebook as well as by emergence of general purpose graph databases. M-Files is a Content Services Platform (CSP) which mostly utilizes relational databases and expressed interest in graph databases. Could M-Files bene-fit from utilizing graph databases? The context of CSP is first established via Enterprise Information Management (EIM). EIM helps in understanding why enterprises have information management needs and how information management software can solve related problems. Then databases used by leading CSP providers are presented to give an overview of the database technology used in leading platforms. Relational databases are the most used and therefore fundamentals of relational databases and graph databases are explained. Graph databases are explained in more detail and are presented with suitable use cases to give a good basic understanding of the technology. A comparison of these databases is presented to emphasize the strengths of graph databases. The strengths are emphasized to show that graph databases do excel with relationship rich data as is claimed. However, there are use cases in which a graph database is not a good option and instead a relational database should be used. These are also presented and help in under-standing whether graph databases should be utilized or not when considering between options. Then M-Files is introduced along with graph database suitable use cases. Possible benefits of utilizing graph databases in M-Files are also presented. A proof-of-concept application which populates a Neo4j graph database with M-Files data was built. The application logic is presented along with used data modeling. Then the Neo4j graph database is compared to a relational database configuration used by M-Files. 5 different queries were each executed 10 times in both databases and the execution times were compared. The queries produced same results in both databases. The application shows that M-Files has some built-in readiness to adopt graph databases. Utilizing graph database technology presents opportunities to innovate for M-Files by enabling a more personalized experience via deeper understanding of the data associated with M-Files. Graph database technology also presents architectural benefits for M-Files by being a viable option for cloud native architecture.Graafitietokannat ovat keränneet suosiota lähivuosina. Yksinoikeudella tuotettujen graafitietokantojen onnistuneet toteutukset kuten Facebookin ja Twitterin graafitietokannat, ovat herättäneet kiinnostusta. Toinen pääsyy kasvaneelle kiinnostukselle on yleiskäyttöisten graafitietokantaratkaisujen parantunut saatavuus. M-Files on sisältöpalvelualusta (CSP eli Content Services Platform) joka hyödyntää relaatiotietokantoja. Voisiko M-Files hyötyä graafitietokantateknologiasta? Aluksi selvennetään yritystiedon hallinnan kautta konteksti ja mitä on CSP. Yritystiedon hallinta selventää miksi yrityksillä on tiedonhallinnan tarpeita ja kuinka tiedonhallintaohjelmistot ratkaisevat tähän liittyviä ongelmia. Sitten esitellään mitä tietokantoja johtavat CSP palveluntarjoajat hyödyntävät. Tämä antaa tilannekuvaa siitä, millaista tietokantateknologiaa johtavat palveluntarjoajat hyödyntävät. Koska relaatiotietokannat ovat yleisimpiä, esitellään ne ja graafitietokannat. Graafitietokannat esitellään yksityiskohtaisemmin, mikä antaa hyvän ymmärryksen kyseisen teknologian perusteista. Graafitietokannoille suotuisat käyttötapaukset esitellään. Kyseisiä tietokantateknologioita vertaillaan korostaakseen graafitietokantojen tiettyjä vahvuuksia. Tämä vertailu osoittaa, että graafitietokannat ovat tehokkaita suhderikkaan datan kanssa, kuten väitetään. Myös käyttötapaukset, jolloin relaatiotietokantojen hyödyntäminen olisi parempi vaihtoehto, esitellään. Tämä auttaa ymmärtämään mitä tietokantaratkaisua kannattaa käyttää, kun harkitsee relaatiotietokantojen ja graafitietokantojen välillä. Vertailun jälkeen itse M-Files esitellään. Sitten esitetään graafitietokantojen mahdollistamia käyttötapauksia, jotka soveltuvat M-Filesin kontekstiin. M-Files toteuttaa jo yhtä näistä käyttötapauksista relaatiotietokantojen avulla. Tähän käyttötapaukseen liittyen esitellään tehty kokeellinen sovellus. Kyseinen sovellus tallentaa M-Filesin dataa Neo4j graafitietokantaan, ja käytetty tiedonmallinnus esitellään. Kyseistä graafitietokantaa vertaillaan M-Filesin käyttämään relaatiotietokantaratkaisuun. Viisi erilaista kyselyä luodaan ja jokainen ajetaan 10 kertaa molemmissa tietokannoissa. Kyselyt tuottavat samat tulokset molemmissa tietokannoissa ja niiden suoritusnopeutta vertaillaan. Kyseinen sovellus osoittaa M-Filesin osittaista valmiutta omaksua graafitietokantojen käyttöä. Graafitietokannat tarjoavat M-Filesille mahdollisuuden innovoida. Kyseinen teknologia mahdollistaa personalisoidumman käyttäjäkokemuksen luomisen. Tämä onnistuu syvemmällä ymmärryksellä datasta, johon M-Filesilla on pääsy. Natiivin pilviarkkitehtuurin kannalta graafitietokannat ovat myös parempi vaihtoehto kuin relaatiotietokannat

Specialized microbial databases for inductive exploration of microbial genome sequences

BACKGROUND: The enormous amount of genome sequence data asks for user-oriented databases to manage sequences and annotations. Queries must include search tools permitting function identification through exploration of related objects. METHODS: The GenoList package for collecting and mining microbial genome databases has been rewritten using MySQL as the database management system. Functions that were not available in MySQL, such as nested subquery, have been implemented. RESULTS: Inductive reasoning in the study of genomes starts from "islands of knowledge", centered around genes with some known background. With this concept of "neighborhood" in mind, a modified version of the GenoList structure has been used for organizing sequence data from prokaryotic genomes of particular interest in China. GenoChore , a set of 17 specialized end-user-oriented microbial databases (including one instance of Microsporidia, Encephalitozoon cuniculi, a member of Eukarya) has been made publicly available. These databases allow the user to browse genome sequence and annotation data using standard queries. In addition they provide a weekly update of searches against the world-wide protein sequences data libraries, allowing one to monitor annotation updates on genes of interest. Finally, they allow users to search for patterns in DNA or protein sequences, taking into account a clustering of genes into formal operons, as well as providing extra facilities to query sequences using predefined sequence patterns. CONCLUSION: This growing set of specialized microbial databases organize data created by the first Chinese bacterial genome programs (ThermaList, Thermoanaerobacter tencongensis, LeptoList, with two different genomes of Leptospira interrogans and SepiList, Staphylococcus epidermidis) associated to related organisms for comparison

Aurinkosähkön tutkimusvoimalan mittaus- ja tiedontallennusjärjestelmä

The photovoltaic research power plant of the Department of Electrical Energy Engineering at the Technical University of Tampere consists of 69 solar modules in different configurations, 21 solar (SP Lite 2) and temperature (Pt100) sensors, data measuring and logging cards (cRIO 9074, NI 9144, 2 NI 9205 and 6 NI 9217) and two weather stations with temperature and humidity sensors (HMP155), wind sensor (WS425) and irradiance sensors (CMP21 and CMP22). This thesis is about connecting these devices together and creating an easy to use and efficient but complex system which processes, collects, saves and provides data from the sensors to researchers. The main goal is to provide researchers with an easy access to the data from the sensors from extended time periods. In the thesis different options and methods to implement the parts of the system have been studied carefully before implementing. The main parts of the system implementation are the following: The data from the sensors are transmitted via cables and extension cables to the data measuring and logging cards which are connected to the local network. Sensors are either grounded from the cable or from the structure. In addition, a lightning rod was built and installed to protect the wind sensor and personnel. The data processing, collecting and saving implementations have mostly been done in LabVIEW. The data are processed by averaging the data to reduce noise and sending it at a 10 Hz rate to the host computer to be saved into a PostgrSQL database. A file-based system alternative to store the data was also implemented. A graphical user interface (GUI) implementation to the MATLAB was made to help researchers gain specific data from the database more easily. A public Internet site was created in the end for the system, and it offers limited general information to everyone who is interested. The system was successfully implemented and completed. It is running 24/7 and ready for researchers to take advantage. Everything in the system has been made with the focus that the system will be expanded, and it should be easy to do so in future. It is recommended to study and understand how the system works before expanding it