Browsing by department "Tietojenkäsittelytieteen laitos"

Open Access -liike pyrkii vapauttamaan tieteellisen tiedon kaupallisuuden rajoitteista edesauttamalla artikkeleiden rinnakkaisversioiden avointa ja esteetöntä verkkotallennusta. Sen mahdollistamiseksi verkkoon perustetaan julkaisuarkistoja, joiden toiminta-ajatuksena on säilöä taustayhteisönsä tieteellinen tuotanto avoimesti ja keskitetysti yhteen paikkaan. Avoimen lähdekoodin arkistosovellukset jakavat sisältönsä OAI-protokollan avulla ja muodostavat näin globaalin virtuaalisen tietoverkon. Suurten tietomäärien käsittelyssä on huomioitava erityisesti kuvailutiedon rooli tehokkaiden hakujen toteuttamisessa sekä tiedon yksilöiminen verkossa erilaisten pysyvien tunnisteiden, kuten Handle:n tai URN:n avulla. Tieteellisen tiedon avoimella saatavuudella on merkittävä vaikutus myös oppimisen näkökulmasta. Julkaisuarkistot tarjoavat oppimateriaalin lisäksi uusia mahdollisuuksia julkaisukanavan ja oppimisymp äristön integroimiseen. Työssä esitellään avoimen saatavuuden keskeisiä teemoja sekä sen käytännön toteutusta varten kehitettyjä teknisiä ratkaisuja. Näiden pohjalta toteutetaan Meilahden kampuksen avoin julkaisuarkisto. Työssä pohditaan myös julkaisuarkistojen soveltuvuutta oppimisprosessin tukemiseen tutkivan- ja sulautuvan oppimisen viitekehyksessä. ACM Computing Classification System (CCS): H.3 [INFORMATION STORAGE AND RETRIEVAL], H.3.7 [Digital Libraries], H.3.3 [Information Search and Retrieval], H.3.5 [Online Information Services], K.3 [COMPUTERS AND EDUCATION], K.3.1 [Computer Uses in Education]

This thesis is a review of bandit algorithms in information retrieval. In information retrieval a result list should include the most relevant documents and the results should also be non-redundant and diverse. To achieve this, some form of feedback is required. This document describes implicit feedback collected from user interactions by using interleaving methods that allow alternative rankings of documents to be presented in result lists. Bandit algorithms can then be used to learn from user interactions in a principled way. The reviewed algorithms include dueling bandits, contextual bandits, and contextual dueling bandits. Additionally coactive learning and preference learning are described. Finally algorithms are summarized by using regret as a performance measure.

The scope of this project is to provide a set of Bayesian methods to be applied to the task of potential energy barriers prediction. Energy barriers define a physical property of atoms that can be used to characterise their molecular dynamics, with applications in quantum-mechanics simulations for the design of new materials. The goal is to replace the currently used artificial neural network (ANN) with a method that apart of providing accurate predictions, can also assess the predictive certainty of the model. We propose several Bayesian methods and evaluate them on this task, demonstrating that sparse Gaussian process (SGP) are capable of providing predictions, and their confidence intervals, with a level of accuracy equivalent to the current ANN, in a bounded computational complexity time.

Continuous integration (CI) and continuous delivery (CD) can be seen as an essential part of modern software development. CI/CD consists of always having software in a deployable state. This is accomplished by continuously integrating the code into a main branch, in addition to automatically building and testing it. Version control and dedicated CI/CD tools can be used to accomplish this. This thesis consists of a case study which aim was to find the benefits and challenges related to the implementation of CI/CD in the context of a Finnish software company. The study was conducted with semi-structured interviews. The benefits of CD that were found include faster iteration, better assurance of quality, and easier deployments. The challenges identified were related to testing practices, infrastructure management and company culture. It is also difficult to implement a full continuous deployment pipeline for the case project, which is mostly due to the risks involved updating software in business-critical production use. The results of this study were found to be similar to the results of previous studies. The case company's adoption of modern CI/CD tools such and GitLab and cloud computing are also discussed. While the tools can make the implementation of CI/CD easier, they still come with challenges in adapting them to specific use cases.

Big data creates variety of business possibilities and helps to gain competitive advantage through predictions, optimization and adaptability. Impact of errors or inconsistencies across the different sources, from where the data is originated and how frequently data is acquired is not considered in much of the big data analysis. This thesis examines big data quality challenges in the context of business analytics. The intent of the thesis is to improve the knowledge of big data quality issues and testing big data. Most of the quality challenges are related to understanding the data, coping with messy source data and interpreting analytical results. Producing analytics requires subjective decisions along the analysis pipeline and analytical results may not lead to objective truth. Errors in big data are not corrected like in traditional data, instead the focus of testing is moved towards process oriented validation.

Business and operational environments are becoming more and more frenetic, forcing companies and organizations to respond to changes faster. This trend reflects to software development as well, IT units have to deliver needed features faster in order to bring business benefits quicker. During the last decade, agile methodologies have provided tools to answer to this ever-growing demand. Scrum is one of the agile methodologies and it is widely used. It is said that in large-scale organizations Scrum implementation should be done using both bottom-up and top-down approaches. In big organizations software systems are complicated and deeply integrated with each other meaning that no one team can handle whole software development processes alone. Individual teams want to start to use Scrum before whole organization is ready to support it. This leads to a situation where one team is applying agile principles while most of the other teams and organizations around are continuing with old established non-agile practices. In these cases bottom-up approach is the only option. When the top-down part is missing, are the benefits also lost? In this case study, the target is to find out, did it bring benefits when implementing Scrum using only bottom-up approach. In the target unit, which was part of the large organization, Scrum based practices were implemented to replace earlier waterfall based approach. Analyses for the study were made on data, which was collected by survey and from a requirement management tool. This tool was in use during the old and new ways of working. Expression Scrum based practices are used because all of the fine flavours of Scrum could not be able to be implemented because of surrounded non-agile teams and official non-agile procedures. This was also an obstacle when trying to implement Scrum as well as it could be possible. Most of the defined targets given to the implementation of Scrum based practices were achieved and other non-targeted benefit came out. In this context we can conclude that benefits were gained. The top-down approach absence clearly made the implementation more difficult and incomplete; however, it didn't prevent to get benefits. The target unit also faced earlier mentioned difficulties in using Scrum based practices while other units around used non-agile processes. The lack of good established numerical estimations of requirements' business values lowered the power of the Scrum on a company level, because these values were relative and subjective opinions of the business representatives, In the backlog prioritization, when most of the items are so called high priority ones there is no way to evaluate which one is more valuable and prioritization is more or less a lottery

With the increase in bandwidths available for internet users, cloud storage services have emerged to offer home users an easy way to share files and extend the storage space available for them. Most systems offer a limited free storage quota and combining these resources from multiple providers could be intriguing to cost-oriented users. In this study, we will implement a virtual file system that utilizes multiple different commercial cloud storage services (Dropbox, Google Drive, Microsoft OneDrive) to store its data. The data will be distributed among the different services and the structure of the data will be managed locally by the file system. The file system will be run in user space using FUSE and will use APIs provided by the cloud storage services to access the data. Our goal is to show that it is feasible to combine the free space offered by multiple services into a single easily accessible storage medium. Building such a system requires making design choices in multiple problem areas ranging from data distribution and performance to data integrity and data security. We will show how our file system is designed to address these requirements and will then conduct several tests to measure and analyze the level of performance provided by our system in different file system operation scenarios. The results will also be compared to the performance of using the distinct cloud storage services directly without distributing the data. This will help us to estimate the overhead or possible gain in performance caused by the distribution of data. It will also help us to locate the bottlenecks of the system. Finally, we will discuss some of the ways that could be used to improve the system based on test results and examples from existing distributed file systems.

Business ecosystems where services from enterprises across the world are marketed and acquired, demand efficient collaborative project management facilities. In particular, reputation and breach management systems are essential in partner selection and proper project delivery. Reputation systems need to provide measurable scales for collection of objective and arbitrable information about members of the ecosystem. In addition, how breaches or disputes can affect reputation of collaborating partners, and how such disputes can be resolved (i.e. breach recovery) are interesting questions. Furthermore, role of business process management (BPM) systems in resolving breach or dispute situations is also an interesting point for study. This thesis proposes modern model-driven reputation and breach management systems of its own, named Reputation and Breach Management System (RAB_MS). Purpose of the RAB_MS is to improve and refine trust between business partners in the business ecosystems. The presented models are based on state of the art techniques of service oriented architecture (SOA). The models are verified by formal automated verification mechanisms in YAWL system, to avoid syntactical, structural, and semantic errors, and interpretation ambiguities. Results of the formal verification mechanisms ensure that the business processes in the proposed reputation and breach management system meet necessary properties such as soundness and weak soundness. In simpler words, RAB_MS have no deadlocks, livelocks, or dead task within its business process models.

Tämä työ tutkii Clojure-ohjelmointikieltä, joka on erityisesti rinnakkaisohjelmointiin suunniteltu Lisp-kielen murre. Clojure tukee vahvaa liitosta Java-ympäristöön ja sillä kirjoitetut ohjelmat suoritetaan JVM-virtuaalikoneella. Tutkielmassa käydään läpi Lisp-kielten historia, rinnakkaisohjelmoinnin yleiset haasteet ja funktionaalisen ohjelmointiparadigman perusteet. Lisäksi käsitellään Java-kielen ja JVM-virtuaalikoneen ja Clojure-kielen rinnakkaisohjelmointipiirteet. Tutkielman analyysiosassa verrataan Clojuren ja Javan rinnakkaisuusratkaisuja muun muassa tehokkuuden ja käytettävyyden osalta. Clojuren rinnakkaisuusratkaisuista transaktiomuisti osoittautui laskennallisesti hyvin raskaaksi. Lisäksi rinnakkaisratkaisujen lukottomuudesta seuraa se, että tietyt rinnakkaisohjelmointiongelmat ovat hankalia toteuttaa ilman, että käytetään Javan rinnakkaisratkaisuja. Erityisesti synkronisten rinnakkaisratkaisujen osalta kielessä olisi kehittämisen varaa. Javaan verrattuna Clojuren rinnakkaisuusratkaisut ovat hieman yksinkertaisempia käyttää. Tämä johtuu kuitenkin pitkälle Clojuren dynaamisesta tyypityksestä ja funktionaalisesta perusrakenteesta.

Online content services can greatly benefit from personalisation features that enable delivery of content that is suited to each user's specific interests. This thesis presents a system that applies text analysis and user modeling techniques in an online news service for the purpose of personalisation and user interest analysis. The system creates a detailed thematic profile for each content item and observes user's actions towards content items to learn user's preferences. A handcrafted taxonomy of concepts, or ontology, is used in profile formation to extract relevant concepts from the text. User preference learning is automatic and there is no need for explicit preference settings or ratings from the user. Learned user profiles are segmented into interest groups using clustering techniques with the objective of providing a source of information for the service provider. Some theoretical background for chosen techniques is presented while the main focus is in finding practical solutions to some of the current information needs, which are not optimally served with traditional techniques.