Kävijätietojen kerääminen ja hyödyntäminen osana verkkopalveluita

Kävijätietojen keräys ja tiedon hyödyntäminen ovat monelle yritykselle yksi lisäkeino saavuttaa kohdeasiakkaansa sekä tarjota heille uusia lisäpalveluita. Kävijätietojen seuraamiselle sekä näiden tietojen hyödyntämiselle löytyy lukuisia käyttökohteita aina hakukoneoptimoinnista uusien asiakkaiden etsimiseen. Moni yritys onkin löytänyt tästä itselleen uuden toimialan. Työn tavoitteena on toteuttaa IP-pohjainen kävijätietojen keräämiseen soveltuva ohjelma, jonka tietoja pystytään hyödyntämään yrityksessä, jonka tuotekonseptiin kuuluu tarjota loppukäyttäjälle maksuttomia hakemisto- ja yhteystietopalveluita. Työssä keskitytään erityisesti ylläpidettävän kävijätietorekisterin luomiseen, mutta esitellään myös, kuinka esimerkiksi yrityksen hakutuloksia voidaan kerätyillä kävijätiedoilla tehostaa sekä tarjota palveluja käyttäville asiakkaille uusia lisäpalveluita. Työssä pyritään hyödyntämään tarjolla olevia avoimiin lisensseihin pohjautuvia ratkaisuja. Tiedonkeruuta toteutettaessa on jo alussa otettava huomioon, että tiedosta ei saada täysin eksaktia, vaan suuntaa-antavaa. Tämä ei kuitenkaan poista sitä, ettei tietoa voisi hyväksikäyttää.

Optimisation of data analysis method for a fluidized bed combustion test rig

Nowadays the used fuel variety in power boilers is widening and new boiler constructions and running models have to be developed. This research and development is done in small pilot plants where more faster analyse about the boiler mass and heat balance is needed to be able to find and do the right decisions already during the test run. The barrier on determining boiler balance during test runs is the long process of chemical analyses of collected input and outputmatter samples. The present work is concentrating on finding a way to determinethe boiler balance without chemical analyses and optimise the test rig to get the best possible accuracy for heat and mass balance of the boiler. The purpose of this work was to create an automatic boiler balance calculation method for 4 MW CFB/BFB pilot boiler of Kvaerner Pulping Oy located in Messukylä in Tampere. The calculation was created in the data management computer of pilot plants automation system. The calculation is made in Microsoft Excel environment, which gives a good base and functions for handling large databases and calculations without any delicate programming. The automation system in pilot plant was reconstructed und updated by Metso Automation Oy during year 2001 and the new system MetsoDNA has good data management properties, which is necessary for big calculations as boiler balance calculation. Two possible methods for calculating boiler balance during test run were found. Either the fuel flow is determined, which is usedto calculate the boiler's mass balance, or the unburned carbon loss is estimated and the mass balance of the boiler is calculated on the basis of boiler's heat balance. Both of the methods have their own weaknesses, so they were constructed parallel in the calculation and the decision of the used method was left to user. User also needs to define the used fuels and some solid mass flowsthat aren't measured automatically by the automation system. With sensitivity analysis was found that the most essential values for accurate boiler balance determination are flue gas oxygen content, the boiler's measured heat output and lower heating value of the fuel. The theoretical part of this work concentrates in the error management of these measurements and analyses and on measurement accuracy and boiler balance calculation in theory. The empirical part of this work concentrates on the creation of the balance calculation for the boiler in issue and on describing the work environment.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Data analysis tools and methods for DNA microarray and high-throughput sequencing data

The recent rapid development of biotechnological approaches has enabled the production of large whole genome level biological data sets. In order to handle thesedata sets, reliable and efficient automated tools and methods for data processingand result interpretation are required. Bioinformatics, as the field of studying andprocessing biological data, tries to answer this need by combining methods and approaches across computer science, statistics, mathematics and engineering to studyand process biological data. The need is also increasing for tools that can be used by the biological researchers themselves who may not have a strong statistical or computational background, which requires creating tools and pipelines with intuitive user interfaces, robust analysis workflows and strong emphasis on result reportingand visualization. Within this thesis, several data analysis tools and methods have been developed for analyzing high-throughput biological data sets. These approaches, coveringseveral aspects of high-throughput data analysis, are specifically aimed for gene expression and genotyping data although in principle they are suitable for analyzing other data types as well. Coherent handling of the data across the various data analysis steps is highly important in order to ensure robust and reliable results. Thus,robust data analysis workflows are also described, putting the developed tools andmethods into a wider context. The choice of the correct analysis method may also depend on the properties of the specific data setandthereforeguidelinesforchoosing an optimal method are given. The data analysis tools, methods and workflows developed within this thesis have been applied to several research studies, of which two representative examplesare included in the thesis. The first study focuses on spermatogenesis in murinetestis and the second one examines cell lineage specification in mouse embryonicstem cells.

Spurring innovation through idea contests : data analysis and process improvements

This research concerns the Urban Living Idea Contest conducted by Creator Space™ of BASF SE during its 150th anniversary in 2015. The main objectives of the thesis are to provide a comprehensive analysis of the Urban Living Idea Contest (ULIC) and propose a number of improvement suggestions for future years. More than 4,000 data points were collected and analyzed to investigate the functionality of different elements of the contest. Furthermore, a set of improvement suggestions were proposed to BASF SE. Novelty of this thesis lies in the data collection and the original analysis of the contest, which identified its critical elements, as well as the areas that could be improved. The author of this research was a member of the organizing team and involved in the decision making process from the beginning until the end of the ULIC.

Heat Demand Forecasting Models’ Development: Use of Data Mining Tools in SQL Server Analysis Services

This thesis introduces heat demand forecasting models which are generated by using data mining algorithms. The forecast spans one full day and this forecast can be used in regulating heat consumption of buildings. For training the data mining models, two years of heat consumption data from a case building and weather measurement data from Finnish Meteorological Institute are used. The thesis utilizes Microsoft SQL Server Analysis Services data mining tools in generating the data mining models and CRISP-DM process framework to implement the research. Results show that the built models can predict heat demand at best with mean average percentage errors of 3.8% for 24-h profile and 5.9% for full day. A deployment model for integrating the generated data mining models into an existing building energy management system is also discussed.

Regional Data Transmission Network

The globalization and development of an information society promptly change shape of the modern world. Cities and especially megacities including Saint-Petersburg are in the center of occuring changes. As a result of these changes the economic activities connected to reception and processing of the information now play very important role in economy of megacities what allows to characterize them as "information". Despite of wide experience in decision of information questions Russia, and in particular Saint-Petersburg, lag behind in development of information systems from the advanced European countries. The given master's thesis is devoted to development of an information system (data transmission network) on the basis of wireless technology in territory of Saint-Petersburg region within the framework of FTOP "Electronic Russia" and RTOP "Electronic Saint-Petersburg" programs. Logically the master's thesis can be divided into 3 parts: 1. The problems, purposes, expected results, terms and implementation of the "Electronic Russia" program. 2. Discussion about wireless data transmission networks (description of technology, substantiation of choice, description of signal's transmission techniques and types of network topology). 3. Fulfillment of the network (organization of central network node, regional centers, access lines, description of used equipment, network's capabilities), financial provision of the project, possible network management models.

Value added logistics in supply and demand chains. Smile. Part 1. eBusiness between global company and itslocal sme supplier network

VALOSADE (Value Added Logistics in Supply and Demand Chains) is the research project of Anita Lukka's VALORE (Value Added Logistics Research) research team inLappeenranta University of Technology. VALOSADE is included in ELO (Ebusiness logistics) technology program of Tekes (Finnish Technology Agency). SMILE (SME-sector, Internet applications and Logistical Efficiency) is one of four subprojects of VALOSADE. SMILE research focuses on case network that is composed of small and medium sized mechanical maintenance service providers and global wood processing customers. Basic principle of SMILE study is communication and ebusiness insupply and demand network. This first phase of research concentrates on creating backgrounds for SMILE study and for ebusiness solutions of maintenance case network. The focus is on general trends of ebusiness in supply chains and networksof different industries; total ebusiness system architecture of company networks; ebusiness strategy of company network; information value chain; different factors, which influence on ebusiness solution of company network; and the correlation between ebusiness and competitive advantage. Literature, interviews and benchmarking were used as research methods in this qualitative case study. Networks and end-to-end supply chains are the organizational structures, which can add value for end customer. Information is one of the key factors in these decentralized structures. Because of decentralization of business, information is produced and used in different companies and in different information systems. Information refinement services are needed to manage information flows in company networksbetween different systems. Furthermore, some new solutions like network information systems are utilised in optimising network performance and in standardizingnetwork common processes. Some cases have however indicated, that utilization of ebusiness in decentralized business model is not always a necessity, but value-add of ICT must be defined case-specifically. In the theory part of report, different ebusiness and architecture models are introduced. These models are compared to empirical case data in research results. The biggest difference between theory and empirical data is that models are mainly developed for large-scale companies - not for SMEs. This is due to that implemented network ebusiness solutions are mainly large company centered. Genuine SME network centred ebusiness models are quite rare, and the study in that area has been few in number. Business relationships between customer and their SME suppliers are nowadays concentrated more on collaborative tactical and strategic initiatives besides transaction based operational initiatives. However, ebusiness systems are further mainly based on exchange of operational transactional data. Collaborative ebusiness solutions are in planning or pilot phase in most case companies. Furthermore, many ebusiness solutions are nowadays between two participants, but network and end-to-end supply chain transparency and information systems are quite rare. Transaction volumes, data formats, the types of exchanged information, information criticality,type and duration of business relationship, internal information systems of partners, processes and operation models (e.g. different ordering models) differ among network companies, and furthermore companies are at different stages on networking and ebusiness readiness. Because of former factors, different customer-supplier combinations in network must utilise totally different ebusiness architectures, technologies, systems and standards.

Artificially Intelligent and Adaptive Methods for Prediction and Analysis of Superheater Fireside Corrosion in Fluidized Bed Boilers

Superheater corrosion causes vast annual losses for the power companies. With a reliable corrosion prediction method, the plants can be designed accordingly, and knowledge of fuel selection and determination of process conditions may be utilized to minimize superheater corrosion. Growing interest to use recycled fuels creates additional demands for the prediction of corrosion potential. Models depending on corrosion theories will fail, if relations between the inputs and the output are poorly known. A prediction model based on fuzzy logic and an artificial neural network is able to improve its performance as the amount of data increases. The corrosion rate of a superheater material can most reliably be detected with a test done in a test combustor or in a commercial boiler. The steel samples can be located in a special, temperature-controlled probe, and exposed to the corrosive environment for a desired time. These tests give information about the average corrosion potential in that environment. Samples may also be cut from superheaters during shutdowns. The analysis ofsamples taken from probes or superheaters after exposure to corrosive environment is a demanding task: if the corrosive contaminants can be reliably analyzed, the corrosion chemistry can be determined, and an estimate of the material lifetime can be given. In cases where the reason for corrosion is not clear, the determination of the corrosion chemistry and the lifetime estimation is more demanding. In order to provide a laboratory tool for the analysis and prediction, a newapproach was chosen. During this study, the following tools were generated: · Amodel for the prediction of superheater fireside corrosion, based on fuzzy logic and an artificial neural network, build upon a corrosion database developed offuel and bed material analyses, and measured corrosion data. The developed model predicts superheater corrosion with high accuracy at the early stages of a project. · An adaptive corrosion analysis tool based on image analysis, constructedas an expert system. This system utilizes implementation of user-defined algorithms, which allows the development of an artificially intelligent system for thetask. According to the results of the analyses, several new rules were developed for the determination of the degree and type of corrosion. By combining these two tools, a user-friendly expert system for the prediction and analyses of superheater fireside corrosion was developed. This tool may also be used for the minimization of corrosion risks by the design of fluidized bed boilers.

Advanced chemometric methods: Applicability on industrial data

Recent years have produced great advances in the instrumentation technology. The amount of available data has been increasing due to the simplicity, speed and accuracy of current spectroscopic instruments. Most of these data are, however, meaningless without a proper analysis. This has been one of the reasons for the overgrowing success of multivariate handling of such data. Industrial data is commonly not designed data; in other words, there is no exact experimental design, but rather the data have been collected as a routine procedure during an industrial process. This makes certain demands on the multivariate modeling, as the selection of samples and variables can have an enormous effect. Common approaches in the modeling of industrial data are PCA (principal component analysis) and PLS (projection to latent structures or partial least squares) but there are also other methods that should be considered. The more advanced methods include multi block modeling and nonlinear modeling. In this thesis it is shown that the results of data analysis vary according to the modeling approach used, thus making the selection of the modeling approach dependent on the purpose of the model. If the model is intended to provide accurate predictions, the approach should be different than in the case where the purpose of modeling is mostly to obtain information about the variables and the process. For industrial applicability it is essential that the methods are robust and sufficiently simple to apply. In this way the methods and the results can be compared and an approach selected that is suitable for the intended purpose. Differences in data analysis methods are compared with data from different fields of industry in this thesis. In the first two papers, the multi block method is considered for data originating from the oil and fertilizer industries. The results are compared to those from PLS and priority PLS. The third paper considers applicability of multivariate models to process control for a reactive crystallization process. In the fourth paper, nonlinear modeling is examined with a data set from the oil industry. The response has a nonlinear relation to the descriptor matrix, and the results are compared between linear modeling, polynomial PLS and nonlinear modeling using nonlinear score vectors.

Lean, Six Sigma ja Total Welding Management hitsaavassa verkostossa

Diplomityössä tarkastellaan hitsaavan verkoston laadunhallintaa ja siinä ilmeneviä erilaisia ongelmakohtia. Tämän lisäksi työssä tarkastellaan kolmen eri laatutyökalun Lean, Six Sigma ja Total Welding Management soveltamista hitsaavan verkoston laadunhallinnassa. Teoriaosassa käsitellään sekä yleisesti että hitsauksen osalta laatua ja laadunhallintaa, sekä edellä mainittuja laatutyökaluja. Tutkimusosaan tietoja hitsaavista verkostoista kerättiin kaikkiaan kolmesta eri verkostosta. Näiden kerättyjen tietojen pohjalta tarkasteltiin valittujen laatutyökalujen soveltuvuutta verkostomaiseen käyttöön. Verkostoitunut toiminta aiheuttaa monia uusia haasteita yritysten laadunhallinnalle verrattuna yksittäisiin hitsaaviin yrityksiin. Suurimpia tutkimuksessa havaittuja ongelmakohtia ovat suunnittelun ja valmistuksen yhteistyön erilaiset puutteet, laatutasoon ja sen varmistukseen liittyvät asiat, sekä verkoston sisälle syntyvä niin sanottu hiljainen tieto ja sen häviäminen. Tutkimuksen tarkastelujen perusteella havaittiin, että kaikkien tutkimukseen valitun kolmen laatutyökalun soveltaminen myös verkostomaisessa toiminnassa on mahdollista, mutta se vaatii huomattavasti suurempaa työpanosta kuin soveltaminen yksittäisessä yrityksessä. Myös näiden kaikkien kolmen työkalun yhtä aikainen käyttö on mahdollista. Juuri oikean työkalun valitseminen kullekin hitsaavalle verkostolle vaatii tarkkaa perehtymistä verkostoon ja sen tilanteeseen.

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.

An IP capable data link layer protocol for narrow band half-duplex packet data radio networks

The wide adaptation of Internet Protocol (IP) as de facto protocol for most communication networks has established a need for developing IP capable data link layer protocol solutions for Machine to machine (M2M) and Internet of Things (IoT) networks. However, the wireless networks used for M2M and IoT applications usually lack the resources commonly associated with modern wireless communication networks. The existing IP capable data link layer solutions for wireless IoT networks provide the necessary overhead minimising and frame optimising features, but are often built to be compatible only with IPv6 and specific radio platforms. The objective of this thesis is to design IPv4 compatible data link layer for Netcontrol Oy's narrow band half-duplex packet data radio system. Based on extensive literature research, system modelling and solution concept testing, this thesis proposes the usage of tunslip protocol as the basis for the system data link layer protocol development. In addition to the functionality of tunslip, this thesis discusses the additional network, routing, compression, security and collision avoidance changes required to be made to the radio platform in order for it to be IP compatible while still being able to maintain the point-to-multipoint and multi-hop network characteristics. The data link layer design consists of the radio application, dynamic Maximum Transmission Unit (MTU) optimisation daemon and the tunslip interface. The proposed design uses tunslip for creating an IP capable data link protocol interface. The radio application receives data from tunslip and compresses the packets and uses the IP addressing information for radio network addressing and routing before forwarding the message to radio network. The dynamic MTU size optimisation daemon controls the tunslip interface maximum MTU size according to the link quality assessment calculated from the radio network diagnostic data received from the radio application. For determining the usability of tunslip as the basis for data link layer protocol, testing of the tunslip interface is conducted with both IEEE 802.15.4 radios and packet data radios. The test cases measure the radio network usability for User Datagram Protocol (UDP) based applications without applying any header or content compression. The test results for the packet data radios reveal that the typical success rate for packet reception through a single-hop link is above 99% with a round-trip-delay of 0.315s for 63B packets.

Developing smart services by Internet of Things in manufacturing business

Manufacturing companies have passed from selling uniquely tangible products to adopting a service-oriented approach to generate steady and continuous revenue streams. Nowadays, equipment and machine manufacturers possess technologies to track and analyze product-related data for obtaining relevant information from customers’ use towards the product after it is sold. The Internet of Things on Industrial environments will allow manufacturers to leverage lifecycle product traceability for innovating towards an information-driven services approach, commonly referred as “Smart Services”, for achieving improvements in support, maintenance and usage processes. The aim of this study is to conduct a literature review and empirical analysis to present a framework that describes a customer-oriented approach for developing information-driven services leveraged by the Internet of Things in manufacturing companies. The empirical study employed tools for the assessment of customer needs for analyzing the case company in terms of information requirements and digital needs. The literature review supported the empirical analysis with a deep research on product lifecycle traceability and digitalization of product-related services within manufacturing value chains. As well as the role of simulation-based technologies on supporting the “Smart Service” development process. The results obtained from the case company analysis show that the customers mainly demand information that allow them to monitor machine conditions, machine behavior on different geographical conditions, machine-implement interactions, and resource and energy consumption. Put simply, information outputs that allow them to increase machine productivity for maximizing yields, save time and optimize resources in the most sustainable way. Based on customer needs assessment, this study presents a framework to describe the initial phases of a “Smart Service” development process, considering the requirements of Smart Engineering methodologies.