In contrast to a traditional setting where users express queries against the database schema, we assert that the semantics of data can often be understood by viewing the data in the context of the user interface (UI) of the software tool used to enter the data. That is, we believe that users will understand the data in a database by seeing the labels, dropdown menus, tool tips, help text, control contents, and juxtaposition or arrangement of controls that are built in to the user interface. Our goal is to allow domain experts with little technical skill to understand and query data. In this dissertation, we present our GUi As View (Guava) framework and describe how we use forms-based UIs to generate a conceptual model that represents the information in the user interface. We then describe how we generate a query interface from the conceptual model. We characterize the resulting query language using a subset of relational algebra. Since most application developers want to craft a physical database to meet desired performance needs independent of the schema used by the user interface, we subsequently present a general-purpose schema mapping tool called a channel that can be configured by instantiating a sequence of discrete transformations. Each transformation is an encapsulation of a physical design decision or business logic process. The channel, once configured, automatically transforms queries from our query interface into queries that address the underlying physical database, similar to a view. The channel also transforms data updates, schema updates, and constraint definitions posed against the channel’s input schema into equivalent forms against the physical schema. We present formal definitions of each transformation and properties that must be true of transformations, and prove that our definitions respect the properties.

In this thesis, available tools for call graph generation, processing and visualization are analyzed. Based on this analysis, a call-graph processing tool is designed. The tool is then implemented and tested on call graphs generated from various real-world programs, including the Linux Kernel.

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Det har blivit vanligare att ge användare val mellan dark mode och light mode, en undersökning görs över hur det påverkar användaren att ha val mellan olika teman samt val av andra aspekter av en webbsidas design. Hur dessa val kan förbättra tillgängligheten för användare med funktionsvariation undersöks och diskuteras. Studien fokuserar på hur dessa val påverkar användare men också den tekniska aspekten av att ha användaralternativ. Två hypoteser kom upp, att version utan val skulle ha snabbast laddningstid och att användare finner det viktigt att ha med användaralternativ. Resultaten visar att den första hypotesen motbevisas medan den andra hypotesen bevisas. Inför framtiden så önskas mer fördjupning och utveckling speciellt då funktionsvariationer alltid kommer att finnas kvar. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p>

Query spelling correction is an important component of modern search engines that can help users to express their intent, and thus improve search quality. In this study, we investigated with what accuracy a sequence-to-sequence recurrent neural network (RNN) can recognise and correct misspellings in a music search engine, when the model is trained with old search queries. A sequence-to-sequence RNN was chosen as the model in this study since it has achieved state-of-the-art performance on similar tasks, such as machine translation and speech recognition. The findings from the study imply that the model learns to correct and complete queries with higher accuracy compared to a baseline model that returns the input query. However, we suggest that, for a model that would be good enough for production, more work needs to be done. Especially, work on creating a cleaner, less biased training dataset. Nevertheless, our work strengthens the idea that sequence-to-sequence RNNs could be used as a spell correction system in search engines. / Stavningskorrigering av söksträngar är en viktig komponent i moderna sökmotorer. Stavningskorrigering kan hjälpa användarna att uttrycka sig och därmed förbättra kvaliteten i sökningen. I det här arbetet undersökte vi med vilken noggrannhet en Recurrent neural network (RNN) modell kan lära sig att korrigera felstavningar i söksträngar från en sökmotor för musik. RNN modellen tränades med söksträngar från historiska sökningar från sökmotorn. Anledningen till att RNN valdes som modell i den här studien var för att den har uppnått hittills bästa möjliga resultat på liknande uppgifter, såsom maskinöversättning och taligenkänning. Resultaten från vår studie visar att modellen lär sig att korrigera och komplettera söksträngar med högre noggrannhet än en basmodell som enbart returnerar indatasträngen. För att utveckla en modell som är tillräckligt bra för produktion föreslår vi emellertid att mer arbete måste utföras. Framför allt är vi övertygade om att ett renare, mindre systematiskt avvikande träningsdataset skulle förbättra modellen. På det hela taget stärker dock vårt arbete hypothesen att RNN modeller kan användas som stavningskorrigeringssystem i sökmotorer.

Private data analysis on statistical data has been addressed by many recent literatures. The goal of such analysis is to measure statistical properties of a database without revealing information of individuals who participate in the database. Differential privacy is a rigorous privacy definition that protects individual information using output perturbation: a differentially private algorithm produces statistically indistinguishable outputs no matter whether the database contains a tuple corresponding to an individual or not. It is straightforward to construct differentially private algorithms for many common tasks and there are published algorithms to support various tasks under differential privacy. However methods to design error-optimal algorithms for most non-trivial tasks are still unknown. In particular, we are interested in error-optimal algorithms for sets of linear queries. A linear query is a sum of counts of tuples that satisfy a certain condition, which covers the scope of many aggregation tasks including count, sum and histogram. We present the matrix mechanism, a novel mechanism for answering sets of linear queries under differential privacy. The matrix mechanism makes a clear distinction between a set of queries submitted by users, called the query workload, and an alternative set of queries to be answered under differential privacy, called the query strategy. The answer to the query workload can then be computed using the answer to the query strategy. Given a query workload, the query strategy determines the distribution of the output noise and the power of the matrix mechanism comes from adaptively choosing a query strategy that minimizes the output noise. Our analyses also provide a theoretical measure to the quality of different strategies for a given workload. This measure is then used in accurate and approximate formulations to the optimization problem that outputs the error-optimal strategy. We present a lower bound of error to answer each workload under the matrix mechanism. The bound reveals that the hardness of a query workload is related to the spectral properties of the workload when it is represented in matrix form. In addition, we design an approximate algorithm, which generates strategies generated by our a out perform state-of-art mechanisms over (epsilon, delta)-differential privacy. Those strategies lead to more accurate data analysis while preserving a rigorous privacy guarantee. Moreover, we also combine the matrix mechanism with a novel data-dependent algorithm, which achieves differential privacy by adding noise that is adapted to the input data and to the given query workload.

Feature engineering at scale is always critical and challenging in the machine learning pipeline. Modern data warehouses enable data analysts to do feature engineering by transforming, validating and aggregating data in Structured Query Language (SQL). To help data analysts do this work, Data Build Tool (DBT), an open-source tool, was proposed to build and orchestrate SQL pipelines. Hopsworks, an open-source scalable feature store, would like to add support for DBT so that data scientists can do feature engineering in Python, Spark, Flink, and SQL in a single platform. This project aims to create a concept about how to build this support and then implement it. The project checks the feasibility of the solution using a sample DBT project. According to measurements, this working solution needs around 800 MB of space in the server and it takes more time than executing DBT commands locally. However, it persistently stores the results of each execution in HopsFS, which are available to users. By adding this novel support for SQL using DBT, Hopsworks might be one of the completest platforms for feature engineering so far. / Att utveckla funktioner i stor skala är alltid kritiskt och utmanande i pipeline för maskininlärning. Moderna datalager gör det möjligt för dataanalytiker att göra feature engineering genom att omvandla, validera och aggregera data i Structured Query Language (SQL). För att hjälpa dataanalytiker att utföra detta arbete föreslogs Data Build Tool (DBT), ett verktyg med öppen källkod, för att bygga och organisera SQL-pipelines. Hopsworks, ett skalbart funktionslager med öppen källkod, vill lägga till stöd för DBT så att datavetare kan göra funktionsutveckling i Python, Spark, Flink och SQL på en enda plattform. Det här projektet syftar till att skapa ett koncept för hur man bygger detta stöd och sedan genomföra det. Projektet kontrollerar lösningens genomförbarhet med hjälp av ett exempel på DBT-projekt. Enligt mätningar behöver denna fungerande lösning cirka 800 MB utrymme på servern och det tar mer tid än att utföra DBT-kommandon lokalt. Den lagrar dock permanent resultaten av varje körning i HopsFS, vilka är tillgängliga för användarna. Genom att lägga till detta nya stöd för SQL med DBT kan Hopsworks vara en av de mest kompletta plattformarna för funktionsutveckling hittills.

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