Identifying Meaningful Places

Place identification refers to the process of analyzing sensor data in order to detect places, i.e., spatial areas that are linked with activities and associated with meanings. Place information can be used, e.g., to provide awareness cues in applications that support social interactions, to provide personalized and location-sensitive information to the user, and to support mobile user studies by providing cues about the situations the study participant has encountered. Regularities in human movement patterns make it possible to detect personally meaningful places by analyzing location traces of a user. This thesis focuses on providing system level support for place identification, as well as on algorithmic issues related to the place identification process. The move from location to place requires interactions between location sensing technologies (e.g., GPS or GSM positioning), algorithms that identify places from location data and applications and services that utilize place information. These interactions can be facilitated using a mobile platform, i.e., an application or framework that runs on a mobile phone. For the purposes of this thesis, mobile platforms automate data capture and processing and provide means for disseminating data to applications and other system components. The first contribution of the thesis is BeTelGeuse, a freely available, open source mobile platform that supports multiple runtime environments. The actual place identification process can be understood as a data analysis task where the goal is to analyze (location) measurements and to identify areas that are meaningful to the user. The second contribution of the thesis is the Dirichlet Process Clustering (DPCluster) algorithm, a novel place identification algorithm. The performance of the DPCluster algorithm is evaluated using twelve different datasets that have been collected by different users, at different locations and over different periods of time. As part of the evaluation we compare the DPCluster algorithm against other state-of-the-art place identification algorithms. The results indicate that the DPCluster algorithm provides improved generalization performance against spatial and temporal variations in location measurements.

Computational Identification of Recessive Mutations in Cancers using High Throughput SNP-arrays

This thesis presents a highly sensitive genome wide search method for recessive mutations. The method is suitable for distantly related samples that are divided into phenotype positives and negatives. High throughput genotype arrays are used to identify and compare homozygous regions between the cohorts. The method is demonstrated by comparing colorectal cancer patients against unaffected references. The objective is to find homozygous regions and alleles that are more common in cancer patients. We have designed and implemented software tools to automate the data analysis from genotypes to lists of candidate genes and to their properties. The programs have been designed in respect to a pipeline architecture that allows their integration to other programs such as biological databases and copy number analysis tools. The integration of the tools is crucial as the genome wide analysis of the cohort differences produces many candidate regions not related to the studied phenotype. CohortComparator is a genotype comparison tool that detects homozygous regions and compares their loci and allele constitutions between two sets of samples. The data is visualised in chromosome specific graphs illustrating the homozygous regions and alleles of each sample. The genomic regions that may harbour recessive mutations are emphasised with different colours and a scoring scheme is given for these regions. The detection of homozygous regions, cohort comparisons and result annotations are all subjected to presumptions many of which have been parameterized in our programs. The effect of these parameters and the suitable scope of the methods have been evaluated. Samples with different resolutions can be balanced with the genotype estimates of their haplotypes and they can be used within the same study.

Drivers of ERP Systems´ Business Value

ERP system implementations have evolved so rapidly that now they represent a must-have within industries. ERP systems are viewed as the cost of doing business. Yet, the research that adopted the resource-based view on the business value of ERP systems concludes that companies may gain competitive advantage when they successfully manage their ERP projects, when they carefully reengineer the organization and when they use the system in line with the organizational strategies. This thesis contributes to the literature on ERP business value by examining key drivers of ERP business value in organizations. The first research paper investigates how ERP systems with different degrees of system functionality are correlated with the development of the business performance after the completion of the ERP projects. The companies with a better perceived system functionality obtained efficiency benefits in the first two years of post-implementation. However, in the third year there is no significant difference in efficiency benefits between successfully and less successfully managed ERP projects. The second research paper examines what business process changes occur in companies implementing ERP for different motivations and how these changes impact the business performance. The findings show that companies reported process changes mainly in terms of workflow changes. In addition, the companies having a business-led motivation focused more on observing average costs of each increase in the input unit. Companies having a technological-led motivation focused more on the benefits coming from the fit of the system with the organizational processes. The third research paper considers the role of alignment between ERP and business strategies for the realization of business value from ERP use. These findings show that strategic alignment and business process changes are significantly correlated with the perceived benefits of ERP at three levels: internal efficiency, customers and financial. Overall, by combining quantitative and qualitative research methods, this thesis puts forward a model that illustrates how successfully managed ERP projects, aligned with the business strategy, have automate and informate effects on processes that ultimately improve the customer service and reduce the companies’ costs.