Supporting Repository Interoperability through Guidelines

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Reusing repository data: migration of data between repositories

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Invenio@HGF: Collaborative repository infrastructure

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Leveraging open access for integrating repository data at Indiana University Libraries

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

When Metadata Collides: Lessons on Combining Records from Multiple Repository Systems.

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Intelligent Internet Data Searching

In this thesis author approaches the problem of automated text classification, which is one of basic tasks for building Intelligent Internet Search Agent. The work discusses various approaches to solving sub-problems of automated text classification, such as feature extraction and machine learning on text sources. Author also describes her own multiword approach to feature extraction and pres-ents the results of testing this approach using linear discriminant analysis based classifier, and classifier combining unsupervised learning for etalon extraction with supervised learning using common backpropagation algorithm for multilevel perceptron.

Data collection and criticality analysis for cost effective maintenance during the life cycle of a flue gas line

Työn tarkoituksena oli kerätä käyttövarmuustietoa savukaasulinjasta kahdelta suomalaiselta sellutehtaalta niiden käyttöönotosta aina tähän päivään asti. Käyttövarmuustieto koostuu luotettavuustiedoista sekä kunnossapitotiedoista. Kerätyn tiedon avulla on mahdollista kuvata tarkasti laitoksen käyttövarmuutta seuraavilla tunnusluvuilla: suunnittelemattomien häiriöiden lukumäärä ja korjausajat, laitteiden seisokkiaika, vikojen todennäköisyys ja korjaavan kunnossapidon kustannukset suhteessa savukaasulinjan korjaavan kunnossapidon kokonaiskustannuksiin. Käyttövarmuustiedon keräysmetodi on esitelty. Savukaasulinjan kriittisten laitteiden määrittelyyn käytetty metodi on yhdistelmä kyselytutkimuksesta ja muunnellusta vian vaikutus- ja kriittisyysanalyysistä. Laitteiden valitsemiskriteerit lopulliseen kriittisyysanalyysiin päätettiin käyttövarmuustietojen sekä kyselytutkimuksen perusteella. Kriittisten laitteiden määrittämisen tarkoitus on löytää savukaasulinjasta ne laitteet, joiden odottamaton vikaantuminen aiheuttaa vakavimmat seuraukset savukaasulinjan luotettavuuteen, tuotantoon, turvallisuuteen, päästöihin ja kustannuksiin. Tiedon avulla rajoitetut kunnossapidon resurssit voidaan suunnata oikein. Kriittisten laitteiden määrittämisen tuloksena todetaan, että kolme kriittisintä laitetta savukaasulinjassa ovat molemmille sellutehtaille yhteisesti: savukaasupuhaltimet, laahakuljettimet sekä ketjukuljettimet. Käyttövarmuustieto osoittaa, että laitteiden luotettavuus on tehdaskohtaista, mutta periaatteessa samat päälinjat voidaan nähdä suunnittelemattomien vikojen todennäköisyyttä esittävissä kuvissa. Kustannukset, jotka esitetään laitteen suunnittelemattomien kunnossapitokustannusten suhteena savukaasulinjan kokonaiskustannuksiin, noudattelevat hyvin pitkälle luotettavuuskäyrää, joka on laskettu laitteen seisokkiajan suhteena käyttötunteihin. Käyttövarmuustiedon keräys yhdistettynä kriittisten laitteiden määrittämiseen mahdollistavat ennakoivan kunnossapidon oikean kohdistamisen ja ajoittamisen laitteiston elinaikana siten, että luotettavuus- ja kustannustehokkuusvaatimukset saavutetaan.

Data Collection Agent Framework

Ohjelmistoteollisuudessa pitkiä ja vaikeita kehityssyklejä voidaan helpottaa käyttämällä hyväksi ohjelmistokehyksiä (frameworks). Ohjelmistokehykset edustavat kokoelmaa luokkia, jotka tarjoavat yleisiä ratkaisuja tietyn ongelmakentän tarpeisiin vapauttaen ohjelmistokehittäjät keskittymään sovelluskohtaisiin vaatimuksiin. Hyvin suunniteltujen ohjelmistokehyksien käyttö lisää suunnitteluratkaisujen sekä lähdekoodin uudelleenkäytettävyyttä enemmän kuin mikään muu suunnittelulähestymistapa. Tietyn kohdealueen tietämys voidaan tallentaa ohjelmistokehyksiin, joista puolestaan voidaan erikoistaa viimeisteltyjä ohjelmistotuotteita. Tässä diplomityössä kuvataan ohjelmistoagentteihin (software agents) perustuvaa ohjelmistokehyksen suunnittelua toteutusta. Pääpaino työssä on vaatimusmäärittelyä vastaavan suunnitelman sekä toteutuksen kuvaaminen ohjelmistokehykselle, josta voidaan erikoistaa erilaiseen tiedonkeruuseen kykeneviä ohjelmistoja Internet ympäristöön. Työn kokeellisessa osuudessa esitellään myös esimerkkisovellus, joka perustuu työssä kehitettyyn ohjelmistokehykseen.

Advanced chemometric methods: Applicability on industrial data

Recent years have produced great advances in the instrumentation technology. The amount of available data has been increasing due to the simplicity, speed and accuracy of current spectroscopic instruments. Most of these data are, however, meaningless without a proper analysis. This has been one of the reasons for the overgrowing success of multivariate handling of such data. Industrial data is commonly not designed data; in other words, there is no exact experimental design, but rather the data have been collected as a routine procedure during an industrial process. This makes certain demands on the multivariate modeling, as the selection of samples and variables can have an enormous effect. Common approaches in the modeling of industrial data are PCA (principal component analysis) and PLS (projection to latent structures or partial least squares) but there are also other methods that should be considered. The more advanced methods include multi block modeling and nonlinear modeling. In this thesis it is shown that the results of data analysis vary according to the modeling approach used, thus making the selection of the modeling approach dependent on the purpose of the model. If the model is intended to provide accurate predictions, the approach should be different than in the case where the purpose of modeling is mostly to obtain information about the variables and the process. For industrial applicability it is essential that the methods are robust and sufficiently simple to apply. In this way the methods and the results can be compared and an approach selected that is suitable for the intended purpose. Differences in data analysis methods are compared with data from different fields of industry in this thesis. In the first two papers, the multi block method is considered for data originating from the oil and fertilizer industries. The results are compared to those from PLS and priority PLS. The third paper considers applicability of multivariate models to process control for a reactive crystallization process. In the fourth paper, nonlinear modeling is examined with a data set from the oil industry. The response has a nonlinear relation to the descriptor matrix, and the results are compared between linear modeling, polynomial PLS and nonlinear modeling using nonlinear score vectors.

Success factors of customer and supplier master data system

Especially in global enterprises, key data is fragmented in multiple Enterprise Resource Planning (ERP) systems. Thus the data is inconsistent, fragmented and redundant across the various systems. Master Data Management (MDM) is a concept, which creates cross-references between customers, suppliers and business units, and enables corporate hierarchies and structures. The overall goal for MDM is the ability to create an enterprise-wide consistent data model, which enables analyzing and reporting customer and supplier data. The goal of the study was defining the properties and success factors of a master data system. The theoretical background was based on literature and the case consisted of enterprise specific needs and demands. The theoretical part presents the concept, background, and principles of MDM and then the phases of system planning and implementation project. Case consists of background, definition of as is situation, definition of project, evaluation criterions and concludes the key results of the thesis. In the end chapter Conclusions combines common principles with the results of the case. The case part ended up dividing important factors of the system in success factors, technical requirements and business benefits. To clarify the project and find funding for the project, business benefits have to be defined and the realization has to be monitored. The thesis found out six success factors for the MDM system: Well defined business case, data management and monitoring, data models and structures defined and maintained, customer and supplier data governance, delivery and quality, commitment, and continuous communication with business. Technical requirements emerged several times during the thesis and therefore those can’t be ignored in the project. Conclusions chapter goes through these factors on a general level. The success factors and technical requirements are related to the essentials of MDM: Governance, Action and Quality. This chapter could be used as guidance in a master data management project.

Data Consistency by a Metadata Management Solution

This thesis consists of three main theoretical themes: quality of data, success of information systems, and metadata in data warehousing. Loosely defined, metadata is descriptive data about data, and, in this thesis, master data means reference data about customers, products etc. The objective of the thesis is to contribute to an implementation of a metadata management solution for an industrial enterprise. The metadata system incorporates a repository, integration, delivery and access tools, as well as semantic rules and procedures for master data maintenance. It targets to improve maintenance processes and quality of hierarchical master data in the case company’s informational systems. That should bring benefits to whole organization in improved information quality, especially in cross-system data consistency, and in more efficient and effective data management processes. As the result of this thesis, the requirements for the metadata management solution in case were compiled, and the success of the new information system and the implementation project was evaluated.

The Role of Knowledge Management in Developing Common Business Mindset in a Firm

Yrityksen yhteisellä liiketoimintanäkemyksellä tarkoitetaan organisaation kykyä ymmärtää liiketoiminnan olennaiset elementit, ja varmistaa, että työntekijöillä ja yrityksen asiakkailla on positiivinen ja yhdenmukainen kuva ja kokemus kyseisestä organisaatiosta. Tämän Pro-gradu – tutkielman tuloksena kehitettiin mittari, jolla yhteisen liiketoimintanäkemyksen tilaa voidaan yrityksessä mitata. Lisäksi tutkielma selvittää tietojohtamisen merkitystä yhteisen liiketoimintanäkemyksen kehityksessä. Tutkimusaineisto kerättiin Internet -kyselytutkimuksella, johon saatiin 158 vastausta. Aineisto analysoitiin tilastollisilla menetelmillä. Tutkimustulokset viittaavat vahvasti siihen, että tiedon jakamisella ja verkostoitumisella on tilastollisesti merkittävä vaikutus yhteisen liiketoimintanäkemyksen kehittymisessä. Tästä syystä yritysten tulisi integroida tietojohtamisen periaatteet strategioihinsa ja luoda systemaattinen malli, joka kannustaa organisaatiota tiedon jakamiseen ja verkostoitumiseen.

MASTER DATA MANAGEMENT MATURITY AND TECHNOLOGY ASSESSMENT - From theory to practice, case Ineo Oy

After decades of mergers and acquisitions and successive technology trends such as CRM, ERP and DW, the data in enterprise systems is scattered and inconsistent. Global organizations face the challenge of addressing local uses of shared business entities, such as customer and material, and at the same time have a consistent, unique, and consolidate view of financial indicators. In addition, current enterprise systems do not accommodate the pace of organizational changes and immense efforts are required to maintain data. When it comes to systems integration, ERPs are considered “closed” and expensive. Data structures are complex and the “out-of-the-box” integration options offered are not based on industry standards. Therefore expensive and time-consuming projects are undertaken in order to have required data flowing according to business processes needs. Master Data Management (MDM) emerges as one discipline focused on ensuring long-term data consistency. Presented as a technology-enabled business discipline, it emphasizes business process and governance to model and maintain the data related to key business entities. There are immense technical and organizational challenges to accomplish the “single version of the truth” MDM mantra. Adding one central repository of master data might prove unfeasible in a few scenarios, thus an incremental approach is recommended, starting from areas most critically affected by data issues. This research aims at understanding the current literature on MDM and contrasting it with views from professionals. The data collected from interviews revealed details on the complexities of data structures and data management practices in global organizations, reinforcing the call for more in-depth research on organizational aspects of MDM. The most difficult piece of master data to manage is the “local” part, the attributes related to the sourcing and storing of materials in one particular warehouse in The Netherlands or a complex set of pricing rules for a subsidiary of a customer in Brazil. From a practical perspective, this research evaluates one MDM solution under development at a Finnish IT solution-provider. By means of applying an existing assessment method, the research attempts at providing the company with one possible tool to evaluate its product from a vendor-agnostics perspective.

Pipework data in design system integration

This thesis work describes the creation of a pipework data structure for design system integration. Work is completed in pulp and paper plant delivery company with global engineering network operations in mind. User case of process design to 3D pipework design is introduced with influence of subcontracting engineering offices. Company data element list is gathered by using key person interviews and results are processed into a pipework data element list. Inter-company co-operation is completed in standardization association and common standard for pipework data elements is found. As result inter-company created pipework data element list is introduced. Further list usage, development and relations to design software vendors are evaluated.

Fedora Commons as core repository of the EU legislation

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014