Business Intelligence handlar om att verksamheter och företag, med hjälp av verktyg och applikationer, ska kunna analysera och visualisera data och information för att sedan med hjälp av denna information kunna fatta verksamhetsnyttiga beslut. Företaget QBIM i Karlstad arbetar med att leverera tjänstebaserad Business Intelligence till kunder i många olika branscher. De sammanställer datan med hjälp av mjukvaran Microsoft Power BI (benämns Power BI), och de levererar reslutatet via bland annat portalen SKI-ANALYTICS. Portalen i nuläget har många brister, och därför kommer detta arbete kretsa kring att identifiera dessa brister och förbättra portalen ur ett användarperspektiv. Detta utförs genom att implementera innehållet i de tre delmålen: inbäddning av Q&A, inbäddning av rapport samt mobilanpassning. Förbättringarna kommer att implementeras med hjälp av Power BI REST API samt Power BI JavaScript API. Slutresultatet är en mer användarvänlig och attraktiv kundportal som är enkel att använda, där de tre delmålen implementerats. Arbetet inkluderar även en diskussion kring fördelar och nackdelar kring användandet av ett färdigt API som lämnar lite frihet till utvecklaren. / Business Intelligence is about helping companies making business beneficial decisions by analysing and visualising data with the help of tools and applications. QBIM is a company in Karlstad that provides their customers with service based BI in various branches. They use a software called Power BI to compile the data from their customers and display the results in their portal SKI-ANALYTICS. The portal has as of now many flaws, and this report will focus on identify and improve those flaws. This goal will be split up and done in three parts; Q&A embedding, report embedding and mobile adaptation. The improvements will be implemented with the use of Power BI REST API and Power BI JavaScript API. The result is a more user friendly and attractive customer portal, where the three part goals are implemented. The report also includes a discussion on pros and cons of the use of an API that severely limits the freedom of the developer.

This thesis describes a way to represent domain knowledge as functions. Those functions can be composed and used for better predicting time needed for a task. These functions can aggregate data from different systems to provide a more complete view of the contextual environment without the need to consolidate data into one system. These functions can be crafted to make a more precise time prediction for a specific task that needs to be carried out in a specific context. We describe a possible way to structure and model data that could be used with the functions. As a proof of concept, a prototype was developed to test an envisioned scenario with simulated data. The prototype is compared to predictions using min, max and average values from previous experience. The result shows that domain knowledge, represented as functions can be used for improved prediction. This way of defining functions for domain knowledge can be used as a part of a CBR system to provide decision support in a problem domain where information about context is available. It is scalable in the sense that more context can be added to new tasks over time and more functions can be added and composed. The functions can be validated on old cases to assure consistency.

Development in the field of software architecture, from the early days in the mid-80’s, has been significant. From purely technical descriptions to decision based architectural knowledge, software architecture has seen fundamental changes to its methodologies and techniques. Architectural knowledge is a resource that is managed and stored by companies, this resource is valuable because it can be reused and analysed to improve future development. Companies today are interested in the reasoning behind the software architecture. This reasoning is mainly formulated through the architectural decisions made during development. For architectural decisions to be easier to analyse they need to be stored in a way that enables use of common analytical tools so that comparisons between decisions are consistent and relevant. Additionally, it is also important to have enough data, which leads us to the problem that, preferably, all the individual architectural knowledge cases must be structured and stored. To do this we present a tool that uses graph generation algorithms to generate architectural knowledge as graphs based on an architectural decision canvas called GRADE. This enables multiple decision cases to be encoded through graphs that can be used to analyse relationships and balances between different architectural knowledge elements represented through nodes and edges within a graph.

No description available.

High profile cyber attacks such as the one on DigiNotar in 2011, where a Certificate Authority (CA) was compromised, has shed light on the vulnerabilities of the internet. In order to make the internet safer in terms of exposing fraudulent certificates, CertificateTransparency (CT) was introduced. The main idea is to append all certificates to a publicly visible log, which anyone can monitor to check for suspicious activity. Although this is a great initiative for needing to rely less on CAs, the logs are still centralized and run by large companies. Therefore, in this thesis, in order to make the logs more available and scalable, we investigate the idea of a distributed client driven CT log via peer-to-peer (P2P) and WebRTC technology that runs in the background of the user’s browser. We show that such a system is indeed implementable, but with limited scalability. We also show that such a system would provide better availability while keeping the integrity of CT by implementing an append only feature, enforced by the Merkle Tree structure.

No description available.

Enterprise Resource Planning systems have been available since the 1990s and come with several business benefits for the users. One of the major advantages is improved decision making through current and accessible information about strategical, tactical and operational levels of the organization. Although several Enterprise Resource Planning system vendors provide several features for contract management, more decision support regarding the total profitability of service contracts is desired by the customers. Estimating the total profitability of service contracts is a challenging task for all service providers and implies a lot of manual data processing by the contract manager. This master’s thesis is conducted in collaboration with IFS World Operations AB and aims to investigate how functionality for budget and forecasting of the profitability of service contracts can be designed to be usable in terms of effectiveness. The implementation was performed iteratively and the resulting prototypes were evaluated and refined throughout the project. The final high-fidelity prototype for budgeting of service contracts was evaluated using the task success rate in conjunction with the System Usability Scale to assess how well the system conformed to the needs of the users. The study revealed that two of the key characteristics of financial follow-up information of service contracts is the support of creating a budget and graphical visualizations of both budgeted and actual values. The final usability evaluation indicated that the developed functionality was usable in terms of effectiveness and has an overall usability clearly above the average.

Denna rapport beskriver ett projektarbete som utfördes av sju studenter från civilingenjörsprogrammen inom datateknik och mjukvaruteknik vid Linköpings universitet.  Projektet utfördes i kursen TDDD96 Kandidatprojekt i mjukvaruutveckling under våren 2018. Syftet med projektet var att utveckla en webbapplikation till Sectra Communications AB som kan användas för att hantera och spåra tillgångar internt hos företaget. Under projektets utvecklingsfas följde projektgruppen en modifierad version av det agila systemutvecklingsramverket Scrum. Projektet resulterade i en fungerande webbapplikation som uppfyller de krav som togs fram tillsammans med Sectra Communications AB. Projektgruppen har utvecklat sina kunskaper inom webbutveckling, agila metoder och att arbeta i grupp. Alla projektmedlemmar har fördjupat sig inom ett varsitt ämne kopplat till projektet, dessa individuella bidrag kan läsas i slutet av rapporten.

We present and discuss algorithms and library software for solving the generalized non-symmetric eigenvalue problem (GNEP) on high performance computing (HPC) platforms with distributed memory. Such problems occur frequently in computational science and engineering, and our contributions make it possible to solve GNEPs fast and accurate in parallel using state-of-the-art HPC systems. A generalized eigenvalue problem corresponds to finding scalars y and vectors x such that Ax = yBx, where A and B are real square matrices. A nonzero x that satisfies the GNEP equation is called an eigenvector of the ordered pair (A,B), and the scalar y is the associated eigenvalue. Our contributions include parallel algorithms for transforming a matrix pair (A,B) to a generalized Schur form (S,T), where S is quasi upper triangular and T is upper triangular. The eigenvalues are revealed from the diagonals of S and T. Moreover, for a specified set of eigenvalues an associated pair of deflating subspaces can be computed, which typically is requested in various applications. In the first stage the matrix pair (A,B) is reduced to a Hessenberg-triangular form (H,T), where H is upper triangular with one nonzero subdiagonal and T is upper triangular, in a finite number of steps. The second stage reduces the matrix pair further to generalized Schur form (S,T) using an iterative QZ-based method. Outgoing from a one-stage method for the reduction from (A,B) to (H,T), a novel parallel algorithm is developed. In brief, a delayed update technique is applied to several partial steps, involving low level operations, before associated accumulated transformations are applied in a blocked fashion which together with a wave-front task scheduler makes the algorithm scale when running in a parallel setting. The potential presence of infinite eigenvalues makes a generalized eigenvalue problem ill-conditioned. Therefore the parallel algorithm for the second stage, reduction to (S,T) form, continuously scan for and robustly deflate infinite eigenvalues. This will reduce the impact so that they do not interfere with other real eigenvalues or are misinterpreted as real eigenvalues. In addition, our parallel iterative QZ-based algorithm makes use of multiple implicit shifts and an aggressive early deflation (AED) technique, which radically speeds up the convergence. The multi-shift strategy is based on independent chains of so called coupled bulges and computational windows which is an important source of making the algorithm scalable. The parallel algorithms have been implemented in state-of-the-art library software. The performance is demonstrated and evaluated using up to 1600 CPU cores for problems with matrices as large as 100000 x 100000. Our library software is described in a User Guide. The software is, optionally, tunable via a set of parameters for various thresholds and buffer sizes etc. These parameters are discussed, and recommended values are specified which should result in reasonable performance on HPC systems similar to the ones we have been running on.

The modern cars have been equipped with advanced technical features to help make driving faster, safer and comfortable. However, to enhance transport security i.e. to avoid unexpected traffic accidents it is necessary to consider a vehicle driver as a part of the environment and need to monitor driver’s health and mental state. Driving behavior-based and physiological parameters-based approaches are the two commonly used approaches to monitor driver’s health and mental state. Previously, physiological parameters-based approaches using sensors are often attached to the human body. Although these sensors attached with body provide excellent signals in lab conditions it can often be troublesome and inconvenient in driving situations.  So, physiological parameters extraction based on video images offers a new paradigm for driver’s health and mental state monitoring. This thesis report presents an intelligent non-contact-based approach to monitor driver’s cognitive load based on physiological parameters and vehicular parameters. Here, camera sensor has been used as a non-contact and pervasive methods for measuring physiological parameters. The contribution of this thesis is in three folds: 1) Implementation of a camera-based method to extract physiological parameters e.g., heart rate (HR), heart rate variability (HRV), inter-bit-interval (IBI), oxygen saturation (SpO2) and respiration rate (RR) considering several challenging conditions e.g. illumination, motion, vibration and movement. 2) Vehicular parameters e.g. lateral speed, steering wheel angle, steering wheel reversal rate, steering wheel torque, yaw rate, lanex, and lateral position extraction from a driving simulator. 3) Investigation of three machine learning algorithms i.e. Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) to classify driver’s cognitive load. Here, according to the results, considering the challenging conditions, the highest correlation coefficient achieved for both HR and SpO2 is 0.96. Again, the Bland Altman plots shows 95% agreement between camera and the reference sensor. For IBI, the quality index (QI) is achieved 97.5% considering 100 ms R-peak error. For cognitive load classification, two separate studies are conducted, study1 with 1-back task and study2 with 2-back task and both time domain and frequency domain features are extracted from the facial videos. Finally, the achieved average accuracy for the classification of cognitive load is 91% for study1 and 83% for study2. In future, the proposed approach should be evaluated in real-road driving environment considering other complex challenging situations such as high temperature, complete dark/bright environment, unusual movements, facial occlusion by hands, sunglasses, scarf, beard etc. / SafeDriver: