Vision based indoor positioning in a retail environment

Modern smart phones often come with a significant amount of computational power and an integrated digital camera making them an ideal platform for intelligents assistants. This work is restricted to retail environments, where users could be provided with for example navigational in- structions to desired products or information about special offers within their close proximity. This kind of applications usually require information about the user's current location in the domain environment, which in our case corresponds to a retail store. We propose a vision based positioning approach that recognizes products the user's mobile phone's camera is currently pointing at. The products are related to locations within the store, which enables us to locate the user by pointing the mobile phone's camera to a group of products. The first step of our method is to extract meaningful features from digital images. We use the Scale- Invariant Feature Transform SIFT algorithm, which extracts features that are highly distinctive in the sense that they can be correctly matched against a large database of features from many images. We collect a comprehensive set of images from all meaningful locations within our domain and extract the SIFT features from each of these images. As the SIFT features are of high dimensionality and thus comparing individual features is infeasible, we apply the Bags of Keypoints method which creates a generic representation, visual category, from all features extracted from images taken from a specific location. A category for an unseen image can be deduced by extracting the corresponding SIFT features and by choosing the category that best fits the extracted features. We have applied the proposed method within a Finnish supermarket. We consider grocery shelves as categories which is a sufficient level of accuracy to help users navigate or to provide useful information about nearby products. We achieve a 40% accuracy which is quite low for commercial applications while significantly outperforming the random guess baseline. Our results suggest that the accuracy of the classification could be increased with a deeper analysis on the domain and by combining existing positioning methods with ours.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.

Simulation and graph mining tools for improving gene mapping efficiency

Gene mapping is a systematic search for genes that affect observable characteristics of an organism. In this thesis we offer computational tools to improve the efficiency of (disease) gene-mapping efforts. In the first part of the thesis we propose an efficient simulation procedure for generating realistic genetical data from isolated populations. Simulated data is useful for evaluating hypothesised gene-mapping study designs and computational analysis tools. As an example of such evaluation, we demonstrate how a population-based study design can be a powerful alternative to traditional family-based designs in association-based gene-mapping projects. In the second part of the thesis we consider a prioritisation of a (typically large) set of putative disease-associated genes acquired from an initial gene-mapping analysis. Prioritisation is necessary to be able to focus on the most promising candidates. We show how to harness the current biomedical knowledge for the prioritisation task by integrating various publicly available biological databases into a weighted biological graph. We then demonstrate how to find and evaluate connections between entities, such as genes and diseases, from this unified schema by graph mining techniques. Finally, in the last part of the thesis, we define the concept of reliable subgraph and the corresponding subgraph extraction problem. Reliable subgraphs concisely describe strong and independent connections between two given vertices in a random graph, and hence they are especially useful for visualising such connections. We propose novel algorithms for extracting reliable subgraphs from large random graphs. The efficiency and scalability of the proposed graph mining methods are backed by extensive experiments on real data. While our application focus is in genetics, the concepts and algorithms can be applied to other domains as well. We demonstrate this generality by considering coauthor graphs in addition to biological graphs in the experiments.

Hakupuut muistihierarkiassa

Algoritmien suoritusajasta kuluu usein merkittävä osa datan siirtelyyn muistihierarkian kerrosten välillä. Ongelma korostuu hakurakenteilla, sillä ne käsittelevät suuria datamääriä. Työn päämääränä on selvittää muistisiirtojen minimoinnilla saavutettavat käytännön edut hakupuiden tapauksessa. Toinen päämäärä on kartoittaa ideaalisen välimuistin malliin perustuvien parametrittomien hakupuiden etuja ja heikkouksia ulkoisen muistin malliin perustuviin parametrillisiin hakupuihin nähden. Parametrittomuus tarkoittaa, ettei algoritmi tiedä käytetyn suunnittelumallin parametreja, kuten välimuistin kokoa. Staattisista hakupuista tarkastellaan leveyssuuntaiseen järjestykseen, esijärjestykseen ja van Emde Boas -järjestykseen tallennettuja binäärihakupuita sekä staattista B-puuta. Dynaamisista hakupuista käsitellään B+-puuta sekä parametritonta B-puuta, COB-puuta. Sekä parametrittomat että parametrilliset hakupuut pyrkivät minimoimaan vaadittavaa muistisiirtojen määrää parantamalla laskennan paikallisuutta. Käsiteltävien hakupuiden käytännön nopeutta testataan monipuolisesti. Saatujen tulosten nojalla sekä staattiset että dynaamiset parametrittomat hakupuut pärjäävät satunnaisoperaatioiden nopeudessa vastaaville parametrillisille hakupuille. Ne jäävät kuitenkin jonkin verran jälkeen perättäisoperaatioita suoritettaessa.

Roskapostin torjunta- ja luokittelumenetelmät

Tässä tutkielmassa tutustutaan kirjallisuuden avulla yleisesti käytössä oleviin roskapostin torjuntamenetelmiin. Myös niitä soveltava järjestelmäkokonaisuus esitellään. Työssä käsitellään esimerkiksi mustat DNS-listat, kollaboratiivisia tekniikoita ja harmaalistaus. Sisältöpohjaisiin menetelmiin, erityisesti bayesiläiseen luokitteluun ja logistiseen regressioanalyysiin tutustutaan tarkemmin. Tutkielmassa perehdytään myös roskapostitusta rajoittavaan lainsäädäntöön ja pohditaan, minkälaisilla keinoilla päädyttäisiin kokonaisuuden kannalta parhaaseen lopputulokseen. Työn kokeellisessa osuudessa verrataan logistista regressioanalyysiä ja bayesiläistä luokittelua roskapostintunnistuksessa realistisella koeasetelmalla käyttäen aitoa sähköpostikorpusta aineistona. Tärkeimmät kokeisiin perustuvat johtopäätökset ovat, että logistiseen regressioanalyysiin pohjaava tunnistus täydentäisi luokittelutuloksen puolesta erinomaisesti roskapostintorjuntajärjestelmää bayesiläisen luokittelijan rinnalla, mutta menetelmänä se on liian hidas tietokantanoudoista johtuvan I/O-vaativuuden takia. Lisäksi todetaan, että jopa käytettyä luokittelumenetelmää tärkeämpi seikka oppivaa roskapostintunnistusta hyödyntävässä järjestelmässä saattaa olla luokittelijalle syötetty aineisto, jonka laadun varmistamiseen on syytä panostaa erityisesti monen käyttäjän roskapostintorjuntajärjestelmässä, jossa luokitellaan kaikkien käyttäjien viestit samaan aineistoon perustuen.

Lean Thinking in Software Development : Impacts of Kanban on Projects

The history of software development in a somewhat systematical way has been performed for half a century. Despite this time period, serious failures in software development projects still occur. The pertinent mission of software project management is to continuously achieve more and more successful projects. The application of agile software methods and more recently the integration of Lean practices contribute to this trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is the operational efficiency of projects. In the field of software development, Kanban as a process management method has gained momentum recently, mostly due to its linkages to Lean thinking. However, only a few empirical studies investigate the impacts of Kanban on projects in that particular area. The aim of this doctoral thesis is to improve the understanding of how Kanban impacts on software projects. The research is carried out in the area of Lean thinking, which contains a variety of concepts including Kanban. This article-type thesis conducts a set of case studies expanded with the research strategy of quasi-controlled experiment. The data-gathering techniques of interviews, questionnaires, and different types of observations are used to study the case projects, and thereby to understand the impacts of Kanban on software development projects. The research papers of the thesis are refereed, international journal and conference publications. The results highlight new findings regarding the application of Kanban in the software context. The key findings of the thesis suggest that Kanban is applicable to software development. Despite its several benefits reported in this thesis, the empirical evidence implies that Kanban is not all-encompassing but requires additional practices to keep development projects performing appropriately. Implications for research are given, as well. In addition to these findings, the thesis contributes in the area of plan-driven software development by suggesting implications both for research and practitioners. As a conclusion, Kanban can benefit software development projects but additional practices would increase its potential for the projects.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.