Distributed document migration between ERP systems by means of messaging and event sourcing

This bachelor’s thesis, written for Lappeenranta University of Technology and implemented in a medium-sized enterprise (SME), examines a distributed document migration system. The system was created to migrate a large number of electronic documents, along with their metadata, from one document management system to another, so as to enable a rapid switchover of an enterprise resource planning systems inside the company. The paper examines, through theoretical analysis, messaging as a possible enabler of distributing applications and how it naturally fits an event based model, whereby system transitions and states are expressed through recorded behaviours. This is put into practice by analysing the implemented migration systems and how the core components, MassTransit, RabbitMQ and MongoDB, were orchestrated together to realize such a system. As a result, the paper presents an architecture for a scalable and distributed system that could migrate hundreds of thousands of documents over weekend, serving its goals in enabling a rapid system switchover.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Sponsor Satisfaction in a Regional Charity Sport Event

The purpose of this Master’s thesis is to study sponsor satisfaction in charity sport events. Lack of research in regional charity sport events, emergence of corporate social responsibility and increasing popularity of charity sport events have created a research gap to be further explored. Theoretical part of the thesis focuses in development of sponsorships, charity sport event sponsorships and sponsorship as a marketing tool. Concept of satisfaction is discussed by implementing marketing theories to weight options on measuring sponsor satisfaction as a part of sponsorship evaluation process. Empirical analysis of the thesis was conducted in a regional charity sport event – Maailman Pisin Salibandyottelu. Evidences were collected in qualitative research method through semi-structured theme interviews. Altogether 12 major and minor sponsors were selected for the primary source of data. The data was analyzed by comparing sponsors’ expectations and experiences, and by displaying sponsors’ perceived satisfaction. The results indicated that sponsors were involved by partly altruistic and partly selfish motives as suggested by previous research. Respondents expressed very few, mainly non-financial expectations, yet were hoping to gain positive image association via event exposure. Negative experiences appear to have relatively small impact in overall satisfaction. Exceeding or fulfilling expectations appears to increase perceived satisfaction which was mainly driven by contribution towards the goodwill, perceived success of the event (successful record attempt, visibility (on- and off-line) and event execution.

The Impact of Event Marketing on Brand Awareness in eSport Industry

The purpose of this qualitative research is to study what is the impact of event marketing on brand awareness in the context of electronic sport industry. Based on the research questions, the theoretical framework will be developed. This research will analyze earlier theories, and also searching more fresh literature to explain the current phenomenon in the eSport industry. In the empirical part, there were total of five case companies interviewed. The context of this research is eSport, which has its own chapter. The theoretical part of the thesis focuses on event marketing and brand awareness. In this research, event marketing is analyzed from the event organizers perspective. In some occasions, event exhibitors’ perspective is also analyzed. In brand awareness, the focus is how to create a brand recognizable, recalled and from there top of mind in consumers’ minds. The results of this research revealed that many companies’ struggles on getting their brand recognizable. Some of the case companies lacks a strategy and don’t exactly know the core values of their customers. However some of the case companies were opposite. One reason behind this is that some of them has experience on the field and the companies have resources that covers them. Also the current strong brand has clearly a positive affect on their business.