On the Role of Metadata in Visual Language Reuse and Reverse Engineering – An Industrial Case

AbstractCollecting metadata on a family of programs is useful not only for generating statistical data on the programs but also for future re-engineering and reuse purposes. In this paper we discuss an industrial case where a project library is used to store visual programs and a database to store the metadata on these programs. The visual language in question is a domain-specific language, Function Block Language (FBL) that is used in Metso Automation for writing automation control programs. For reuse, program analysis and re-engineering activities and various data and program analysis methods are applied to study the FBL programs. Metadata stored in a database is used to provide advanced program analysis support; from the large amount of programs, the metadata allows focusing the analysis to certain kinds of programs. In this paper, we discuss the role and usage of the metadata in program analysis techniques applied to FBL programs

Tarjouskirjan stokastinen mallintaminen

Tässä kandidaatintyössä käsitellään tarjouskirjan stokastista mallintamista, mikä yhdistää useita eri matematiikan osa-alueita. Työssä käsitellään yhtä keskeistä mallia syvällisesti, mikä johtaa ymmärrykseen tarjouskirjan mallintamisesta ja mallin kritisointiin empiiristen tutkimusten ja vaihtoehtoisten mallien pohjalta. Työ on toteutettu kirjallisuuskatsauksena eli malleja on vertailtu kvalitatiivisesti ja selkeitä kehityskohteita on esitetty muiden tutkimusten pohjalta. Tarjouskirjan mallintamisessa tärkeitä mallinnettavia asioita ovat osto- ja myyntihinta, tarjouksien määrät eri hinnoilla ja todennäköisyydet, joilla muutoksia tapahtuu tarjouskirjassa. Työssä tarjouskirja on ensin esitetty matemaattisin merkinnöin, minkä jälkeen näille tärkeille ominaisuuksille on esitetty matemaattiset kaavat tulevaisuuteen. Samalla malliin on esitetty kehityskohteita, kuten esimerkiksi parametrien estimoinnissa vaihtoehtoisia tapoja löytää parhaat parametrit. Työssä esitetään matemaattiset laskukaavat todennäköisyyksille, joilla keskihinta nousee tai laskee, rajahintatarjous toteutuu ennen hinnan muuttumista ja markkinantakaaja tuottaa hintaeron. Näiden todennäköisyyksien esittäminen mahdollistaa eri kaupankäyntistrategioiden kehittämisen ja työssä esitellään näistä kaksi yksinkertaista vaihtoehtoa. Ensin työssä esitetään miten osake kannattaa ostaa ja myydä myöhemmin, jos hinnan nousemiselle on korkea todennäköisyys, ja tämän jälkeen hintaeron tuottamiselle esitetään strategia. Mallin vertaaminen vaihtoehtoisiin malleihin on toteutettu etsimällä mahdollisimman monipuolisia tapoja mallintaa tarjouskirjaa. Tässä ideana on ollut löytää eri kehityskohteita esitettyyn malliin, jotta mahdollinen suurin syy epätarkalle tulokselle voitaisiin löytää. Vaihtoehtoisista malleista löydettiin eroavaisuuksia tarjouksien yksittäisestä tarkastelusta, tapahtumien välisistä korrelaatioista, differentiaaliyhtälöiden tärkeydestä, oletetusta jakaumasta, ratkaisun analyyttisyydestä, volatiliteetin vaikutuksesta, taloustieteellisestä selityksestä ja omien toimintojen vaikutuksesta tarjouskirjaan. Näiden havaintojen perusteella malliin mietittiin mahdollisia kehityskohteita, jotka olisivat jatkotutkimuksen kohteina.This bachelor’s thesis studies stochastic order book modelling, which includes many different subjects from mathematics. The thesis studies a single stochastic order book model more deeply, which helps with understanding order book modelling and in criticizing the model. The criticizing is done by comparing the model to other models and empirical studies. The bachelor’s thesis is a literature review, and thus it is qualitative in nature and the ideas for further improvements are based on other studies. The most important values in order book modelling are ask and bid prices, number of orders on each price, and probabilities of different changes in the order book. In the thesis, the order book is first introduced with mathematical notation, which is followed by the equations of different probabilities of changes. Furthermore, while the order book is introduced, different improvement ideas are introduced. For example, when estimating parameters for the model, one could use different methods to get better results. The three equations of probabilities are introduced for increase in mid-price, executing order before mid-price moves and making the spread. The introduction of the equations makes it possible to use them in simple trading strategies of which two are introduced. In the first one, a market participant buys a stock and sells it later if there is a high probability of increase in the mid-price. In the second one, a market participant should enter two limit book orders if there is a high probability of making the spread. Different models are briefly introduced to compare them to the main model. The different models are as different as possible to get maximum utility from them for the main model. The different models differ in the use of unit size in order sizes, the correlations between different changes, the importance of differential equations, the assumption of the probability distribution, the analytical solution, the effect of volatility, the economic explanation and how a market participant’s actions affect the order book. These observations were used to make propositions for further improvements in the model

Design Objectives for Evolvable Knowledge Graphs

Knowledge graphs (KGs) structure knowledge to enable the development of intelligent systems across several application domains. In industrial maintenance, comprehensive knowledge of the factory, machinery, and components is indispensable. This study defines the objectives for evolvable KGs, building upon our prior research, where we initially identified the problem in industrial maintenance. Our contributions include two main aspects: firstly, the categorization of learning within the KG construction process and the identification of design objectives for the KG process focusing on supporting industrial maintenance. The categorization highlights the specific requirements for KG design, emphasizing the importance of planning for maintenance and reuse

The role of social media influencer characteristics on consumer behaviour

Objectives The main objectives of this study were to identify the main characteristics of social media influencers that impact consumer behaviour and to assess the impact of the main characteristics of social media influencers on consumer behaviour. In addition, this study explored if the type of game, indie or AAA, impacts the relationship between social media influencer characteristics and consumer behaviour. Summary Literature on influencer marketing, characteristics of social media influencer and consumer behaviour were explored to create a conceptual framework for the study. Next, a survey was created to pre-test how characteristics were perceived, which helped creating a survey to research if attractiveness and trustworthiness impacted consumer behavioural outcomes and if type of game impacts the relationship. Conclusions Attractiveness of social media influencers was found to positively impact word- of-mouth, purchase intent, attitude towards streamer and attitude towards indie games. Trustworthiness of social media influencers was found to positively impact word-of-mouth, purchase intent, attitude towards the streamer, attitude towards indie and AAA games. Type of game did not function as a moderating variable between characteristics and consumer behaviour. Also, important traits of social media influencers were identified, which can help managers in choosing influencers for marketing purposes

Tarjouskirjan stokastinen mallintaminen

Tässä kandidaatintyössä käsitellään tarjouskirjan stokastista mallintamista, mikä yhdistää useita eri matematiikan osa-alueita. Työssä käsitellään yhtä keskeistä mallia syvällisesti, mikä johtaa ymmärrykseen tarjouskirjan mallintamisesta ja mallin kritisointiin empiiristen tutkimusten ja vaihtoehtoisten mallien pohjalta. Työ on toteutettu kirjallisuuskatsauksena eli malleja on vertailtu kvalitatiivisesti ja selkeitä kehityskohteita on esitetty muiden tutkimusten pohjalta. Tarjouskirjan mallintamisessa tärkeitä mallinnettavia asioita ovat osto- ja myyntihinta, tarjouksien määrät eri hinnoilla ja todennäköisyydet, joilla muutoksia tapahtuu tarjouskirjassa. Työssä tarjouskirja on ensin esitetty matemaattisin merkinnöin, minkä jälkeen näille tärkeille ominaisuuksille on esitetty matemaattiset kaavat tulevaisuuteen. Samalla malliin on esitetty kehityskohteita, kuten esimerkiksi parametrien estimoinnissa vaihtoehtoisia tapoja löytää parhaat parametrit. Työssä esitetään matemaattiset laskukaavat todennäköisyyksille, joilla keskihinta nousee tai laskee, rajahintatarjous toteutuu ennen hinnan muuttumista ja markkinantakaaja tuottaa hintaeron. Näiden todennäköisyyksien esittäminen mahdollistaa eri kaupankäyntistrategioiden kehittämisen ja työssä esitellään näistä kaksi yksinkertaista vaihtoehtoa. Ensin työssä esitetään miten osake kannattaa ostaa ja myydä myöhemmin, jos hinnan nousemiselle on korkea todennäköisyys, ja tämän jälkeen hintaeron tuottamiselle esitetään strategia. Mallin vertaaminen vaihtoehtoisiin malleihin on toteutettu etsimällä mahdollisimman monipuolisia tapoja mallintaa tarjouskirjaa. Tässä ideana on ollut löytää eri kehityskohteita esitettyyn malliin, jotta mahdollinen suurin syy epätarkalle tulokselle voitaisiin löytää. Vaihtoehtoisista malleista löydettiin eroavaisuuksia tarjouksien yksittäisestä tarkastelusta, tapahtumien välisistä korrelaatioista, differentiaaliyhtälöiden tärkeydestä, oletetusta jakaumasta, ratkaisun analyyttisyydestä, volatiliteetin vaikutuksesta, taloustieteellisestä selityksestä ja omien toimintojen vaikutuksesta tarjouskirjaan. Näiden havaintojen perusteella malliin mietittiin mahdollisia kehityskohteita, jotka olisivat jatkotutkimuksen kohteina.This bachelor’s thesis studies stochastic order book modelling, which includes many different subjects from mathematics. The thesis studies a single stochastic order book model more deeply, which helps with understanding order book modelling and in criticizing the model. The criticizing is done by comparing the model to other models and empirical studies. The bachelor’s thesis is a literature review, and thus it is qualitative in nature and the ideas for further improvements are based on other studies. The most important values in order book modelling are ask and bid prices, number of orders on each price, and probabilities of different changes in the order book. In the thesis, the order book is first introduced with mathematical notation, which is followed by the equations of different probabilities of changes. Furthermore, while the order book is introduced, different improvement ideas are introduced. For example, when estimating parameters for the model, one could use different methods to get better results. The three equations of probabilities are introduced for increase in mid-price, executing order before mid-price moves and making the spread. The introduction of the equations makes it possible to use them in simple trading strategies of which two are introduced. In the first one, a market participant buys a stock and sells it later if there is a high probability of increase in the mid-price. In the second one, a market participant should enter two limit book orders if there is a high probability of making the spread. Different models are briefly introduced to compare them to the main model. The different models are as different as possible to get maximum utility from them for the main model. The different models differ in the use of unit size in order sizes, the correlations between different changes, the importance of differential equations, the assumption of the probability distribution, the analytical solution, the effect of volatility, the economic explanation and how a market participant’s actions affect the order book. These observations were used to make propositions for further improvements in the model

Money Flows Between Securities: Network Analysis in a Stock Market

Application of network science to study various phenomena has increased in the recent years as for example networks have been used to model the effects of age to the spread of COVID-19, how the World Trade Web changed during the financial crisis of 2008 and to rank web pages. Networks have been used to study financial markets although research has focused mainly on interbank lending. The financial crisis of 2008 has been shown to have happened partly because of the financial industry, and the interbank lending networks showed early-warning signals of the crisis. In 2008 stock markets crashed and investors changed their allocations to different assets based on their views of the future. Therefore, money flowed between securities which can be modelled using networks, and network science offers tools to study the topological features. The goals of the thesis were to define money flow networks and to study possible changes in the networks during the financial crisis of 2008 in Finland. Money flows to securities have been used before as a technical indicator but the sources of the flows have not been incorporated to the analyses. Thus, a method for approximating money flows between securities from transaction data is defined which forms a network of money flows between securities. To compare the money flows, the absolute money flows are scaled using the largest money flow during the last 90 days between two securities. The networks of financial institutions and households are analyzed by ranking the securities based on their centralities and by analyzing changes in the z-scores of subgraph abundancies. The ranking of securities based on their centralities is used to find out if some securities are favoured or neglected during the crisis, and different centrality measures are used with statistical testing to ensure the results. The z-scores of subgraph abundancies are used to find general changes in the structure of the networks during the crisis. Since the subgraph counts are affected by the number of links and degrees of nodes, two different random graph models are used in calculating the deviations from the expected subgraph counts. The centrality rankings showed that large companies are more often in the top percentiles of the rankings while the bottom percentiles mostly do not have certain securities in them more often than expected. Finance institutes had more random ranking than households as in the daily networks households did not invest in smaller companies as often. The z-scores of subgraph abundancies had multiple observations and interpretations, but further analyses are needed to understand the changes better. Household networks had more complex structure than finance institute networks which may mean households had less similar opinions about the securities. Based on the analyses, the centrality rankings failed in finding securities with unusual money flows, even though differences between investing of finance institutes and households were found, and there were changes in the structure of the networks during the crisis. In the end, money flow networks should be studied further, and future analyses should incorporate additional market data