The main purpose of this study was to determine preservice science teachers&rsquo / (PSTs) epistemological beliefs regarding the nature of knowledge and learning in the domain of environment through comparing with the domains of biology, physics, chemistry, and mathematics. A total of 12 PSTs voluntarily participated in the study. The sample of this study was consisted of senior elementary PSTs who registered for an elective course titled &ldquo / Laboratory Applications in Science and Environmental Education&rdquo / in the fall semester of 2008-2009 at a public university, in Ankara. The major data of this study was collected by using a semi-structured interview protocol, developed by Schommer-Aikins (2008). The data of this study were analyzed through descriptive statistics and Miles and Huberman approach (1994). The data analyses of this study were presented along with five dimensions of epistemological beliefs. The analysis of omniscient authority indicated that the PSTs less trust in environmental experts&rsquo / opinions, give more importance to informal education in the acquisition of environmental knowledge, and believe that environmental knowledge is justified more on the basis of direct observation. The analysis of stability of knowledge revealed that the PSTs conceived of environmental knowledge as more uncertain. The analysis of structure of knowledge pointed out that the PSTs consider environmental knowledge as more complex. The analysis of control of learning revealed that the PSTs believe that the large percentage of ability to learn can be acquired after the birth more in environment. The analysis of speed of learning indicated that the PSTs believe that much of learning takes less time in the domain of environment. This study provided evidence that epistemological beliefs are multidimensional and domain-specific. Moreover, this study highlighted that the nature of environmental knowledge and learning are also an important issue to be addressed in environmental education.

Viena iš naujausių sistemų kūrimo metodikų – sričiai orientuotas modeliavimas (angl. domain specific modeling). Ji paremta sričiai orientuotos kalbos kūrimu ir jos panaudojimu sistemos kūrimui. Ši metodika leidžia sistemos kūrimui naudoti sąvokas, artimas probleminei sričiai, kas padidina abstrakcijos lygi projektavimo metu. Srities sąvokos aptariamos ir įvardinamos kartu su ekspertais ar paprastais darbuotojais, dirbančiais analizuojamoje srityje, ir nebūtinai išmanančiais sistemų kūrimo metodus. Ši metodika leidžia sistemos projektuotojui lengviau susikalbėti su užsakovais, nes modeliuose figūruojančios sąvokos yra užsakovui suprantamos ir aiškios. / Domain Specific Modeling is a software engineering methodology for designing and developing information systems. It involves systematic use of a graphical domain specific language (DSL) to represent the various facets of a system. DSM languages tend to support higher-level abstractions than general purpose modeling languages, so they require less effort and fewer low-level details to specify a given system which is very important nowadays, when the functionality of information system must be maximized with minimum development time and cost. This paper compares usage of DSL for information system development with other two, most often used methodologies for this purpose: MDA and usage of UML. It gives basic advantages and disadvantages of DSL and DSM usage, describes differences of DSL (DSM), UML and MDA. This paper also describes usage of DSM methodology (creating DSL and using it for software development) for creating information system. It covers main steps of creation process’s: describing the domain concepts, describing the artifacts that are planning for the DSL, building domain model, building the designer for DSL, building the artifact generator, implementing validations and constraints, testing and deploying the DSL.

Testing is an important part of software development. It ensures that the developed product is of high standard and quality. Tieto frequently develops large complex systems which require comprehensive testing. Testing employs manually designed test cases. According to recent development within software testing it has been shown that design of test cases can be simplified with domain specific test languages (DSTL).     The thesis project is a research and development study in the field of software testing and has been performed at Tieto office in Karlstad. The project concerns the development of a DSTL and a suitable development environment based on the Eclipse Platform. The project evaluates the development of a domain specific test language using the tools Eclipse JDT, developed by Eclipse Foundation, and Xtext, developed by itemis AG. The experiment was evaluated by its functionality and integrability.     The project has shown promise in using a domain specific test language with a suitable development environment. The result and evaluation have shown that the subject shows promise, but needs further development if it is to be adapted within Tieto´s organization.

Scientific workflow systems enable scientists to perform large-scale data intensive scientific experiments using distributed computing resources. Due to the diversity of domains and complexity of technology, delivering a successful outcome efficiently requires collaboration between domain experts and technical experts. However, existing scientific workflow systems require a large investment of time to familiarise and adapt existing workflows. Thus, many scientific workflows are still being implemented by script based languages (such as Python and R) due to familiarity and extensive third party library support. In this thesis, we implement a framework that uses a domain specific language that enables domain experts to collaborate on fine-tuning workflows. Technical experts are able to use Python for task implementations. Moreover, the framework includes support for parallel execution without any specialized code. It also provides a provenance capturing framework that enables users to analyse past executions and retrieve complete lineage of any data item generated. Experiments which were performed using a real-world scientific workflow from the bioinformatics domain show that users were able to execute workflows efficiently while using our DSL for workflow composition and Python for task implementations. Moreover, we show that captured provenance can be useful for analysing past workflow executions. / Vetenskapliga arbetsflödessystem gör det möjligt för forskare att utföra storskaliga dataintensiva vetenskapliga experiment med hjälp av distribuerade datorresurser. På grund av mångfalden av domäner, och komplexitet i teknik, krävs samarbete mellan domänexperter och tekniska experter för att på ett effektivt sätt leverera en framgångsrik lösning. Befintliga vetenskapliga arbetsflödessystem kräver dock en stor investering i tid för att bekanta och anpassa befintliga arbetsflöden. Som ett resultat av detta implementeras många vetenskapliga arbetsflöden fortfarande av skriptbaserade språk (som Python och R) på grund av förtrogenhet och omfattande support från tredje part. I denna avhandling implementeras ett framework som använder ett domänsspecifikt språk som gör det möjligt för domänexperter att samarbeta med att finjustera arbetsflöden. Tekniska experter kan använda Python för att genomföra uppgifter. Dessutom innehåller ramverket stöd för parallell exekvering utan någon specialkod. Detta ger också ett ursprungsfångande framework som gör det möjligt för användare att analysera tidigare exekveringar och att hämta fullständiga härstamningar för samtliga genererade dataobjekt. Experiment som utfördes med hjälp av ett verkligt vetenskapligt arbetsflöde från bioinformatikdomänen visar att användarna effektivt kunde utföra arbetsflöden medan de använde en DSL för arbetsflödesammansättning och Python för uppdragsimplementationer. Dessutom visar vi hur fångade ursprung kan vara användbara för att analysera tidigare genomförda arbetsflödesexekveringar.

Goal-Oriented Modelling Languages such as the Goal Requirements Language (GRL) have been used to reason about Design Patterns. However, the GRL is a general purpose modelling language that does not support concepts bespoke to the pattern domain. This thesis has investigated how advanced programming language techniques, namely Dependent Types and Domain Specific Languages, can be used to enhance the design and construction of Domain Specific Modelling languages (DSMLs), and apply the results to Design Pattern Engineering. This thesis presents Sif, a DSML for reasoning about design patterns as goal- oriented requirements problems. Sif presents modellers with a modelling language tailored to the pattern domain but leverages the GRL for realisation of the modelling constructs. Dependent types have influenced the design and implementation of Sif to provide correctness guarantees, and have led to the development of NovoGRL a novel extension of the GRL. A technique for DSML implementation called Types as (Meta) Modellers was developed in which the interpretation between a DSML and its host language is implemented directly within the type-system of the DSML. This provides correctness guarantees of DSML model instances during model construction. Models can only be constructed if and only if the DSML's type-system can build a valid representation of the model in the host language. This thesis also investigated design pattern evaluation, developing PREMES an evaluation framework that uses tailorable testing techniques to provide demonstrable reporting on pattern quality. Linking PREMES with Sif are: Freyja—an active pattern document schema in which Sif models are embedded within pattern documents; and Frigg—a tool for interacting with pattern documents. The proof-of-concept tools in this thesis demonstrate: machine enhanced interactions with design patterns; reproducible automation in the PREMES framework; and machine checking of pattern documents as Sif models. With the tooling and techniques presented, design pattern engineering can become a more rigorous, demonstrable, and machine checkable process.

Traditional data management technologies originating from business domain are currently facing many challenges from other domains such as scientific research. Data structures in databases are becoming more and more complex and data query functions are moving from the back-end database level towards the front-end user-interface level. Traditional query languages such as SQL, OQL, and form-based query interfaces cannot fully meet the needs today. This research is motivated by the data management issues in life science applications. I propose a methodology for domain-specific conceptual data modeling and querying. The methodology can be applied to any domain to capture more domain semantics and empower end-users to formulate a query at the conceptual level with terminologies and functions familiar to them. The query system resulting from the methodology is designed to work on all major types of database management systems (DBMS) and support end-users to dynamically define and add new domain-specific functions. That is, all user-defined functions can be either pre-defined by domain experts and/or data model creators at the time of system creation, or dynamically defined by end-users from the client side at any time. The methodology has a domain-specific conceptual data model (DSC-DM) and a domain-specific conceptual query language (DSC-QL). DSC-QL uses only the abstract concepts, relationships, and functions defined in DSC-DM. It is a user-oriented high level query language and intentionally designed to be flexible, extensible, and readily usable. DSC-QL queries are much simpler than corresponding SQL or OQL queries because of advanced features such as user-defined functions, composite and set attributes, dot-path expressions, and super-classes. DSC-QL can be translated into SQL and OQL through a dynamic mapping function, and automatically updated when the underlying database schema evolves. The operational and declarative semantics of DSC-QL are formally defined in terms of graphs. A normal form for DSC-QL as a standard format for the mappings from flexible conceptual expressions to restricted SQL or OQL statements is also defined. Two translation algorithms from normalized DSC-QL to SQL and OQL are introduced. Through comparison, DSC-QL is shown to have very good balance between simplicity and expressive power and is suitable for end-users. Implementation details of the query system are reported as well. Two prototypes have been built. One prototype is for neuroscience domain, which is built on an object-oriented DBMS. The other one is for traditional business domain, which is built on a relational DBMS.

With advancement in the development of the new laboratory instruments and experimental techniques, the protein data has an explosive increasing rate. Therefore how to efficiently store, retrieve and modify protein data is becoming a challenging issue that most biological scientists have to face and solve. Traditional data models such as relational database lack of support for complex data types, which is a big issue for protein data application. Hence many scientists switch to the object-oriented databases since object-oriented nature of life science data perfectly matches the architecture of object-oriented databases, but there are still a lot of problems that need to be solved in order to apply OODB methodologies to manage protein data. One major problem is that the general-purpose OODBs do not have any built-in data types for biological research and built-in biological domain-specific functional operations. In this dissertation, we present an application system with built-in data types and built-in biological domain-specific functional operations that extends the Object-Oriented Database (OODB) system by adding domain-specific additional layers Protein-QL, Protein Algebra Architecture and Protein-OODB above OODB to manage protein structure data. This system is composed of three parts: 1) Client API to provide easy usage for different users. 2) Middleware including Protein-QL, Protein Algebra Architecture and Protein-OODB is designed to implement protein domain specific query language and optimize the complex queries, also it capsulates the details of the implementation such that users can easily understand and master Protein-QL. 3) Data Storage is used to store our protein data. This system is for protein domain, but it can be easily extended into other biological domains to build a bio-OODBMS. In this system, protein, primary, secondary, and tertiary structures are defined as internal data types to simplify the queries in Protein-QL such that the domain scientists can easily master the query language and formulate data requests, and EyeDB is used as the underlying OODB to communicate with Protein-OODB. In addition, protein data is usually stored as PDB format and PDB format is old, ambiguous, and inadequate, therefore, PDB data curation will be discussed in detail in the dissertation.

Experimental game theory is the use of game theoretic abstractions—games, players, and strategies—in experiments and simulations. It is often used in cases where traditional, analytical game theory fails or is difficult to apply. This thesis collects three previously published papers that provide domain-specific language (DSL) support for defining and executing these experiments, and for explaining their results. Despite the widespread use of software in this field, there is a distinct lack of tool support for common tasks like modeling games and running simulations. Instead, most experiments are created from scratch in general-purpose programming languages. We have addressed this problem with Hagl, a DSL embedded in Haskell that allows the concise, declarative definition of games, strategies, and executable experiments. Hagl raises the level of abstraction for experimental game theory, reducing the effort to conduct experiments and freeing experimenters to focus on hard problems in their domain instead of low-level implementation details. While analytical game theory is most often used as a prescriptive tool, a way to analyze a situation and determine the best course of action, experimental game theory is often applied descriptively to explain why agents interact and behave in a certain way. Often these interactions are complex and surprising. To support this explanatory role, we have designed visual DSL for explaining the interaction of strategies for iterated games. This language is used as a vehicle to introduce the notational quality of traceability and the new paradigm of explanation-oriented programming. / Graduation date: 2012

Design synthesis is a fundamental engineering task that involves the creation of structure from a desired functional specification; it involves both creating a system topology as well as sizing the system's components. Although the use of computer tools is common throughout the design process, design synthesis is often a task left to the designer. At the synthesis stage of the design process, designers have an extensive choice of design alternatives that need to be considered and evaluated. Designers can benefit from computational synthesis methods in the creative phase of the design process. Recent increases in computational power allow automated synthesis methods for rapidly generating a large number of design solutions. Combining an automated synthesis method with an evaluation framework allows for a more thorough exploration of the design space as well as for a reduction of the time and cost needed to design a system. To facilitate computational synthesis, knowledge about feasible system configurations must be captured. Since it is difficult to capture such synthesis knowledge about any possible system, a design domain must be chosen. In this thesis, the design domain is hydraulic systems. In this thesis, Model-Driven Software Development concepts are leveraged to create a framework to automate the synthesis of hydraulic systems will be presented and demonstrated. This includes the presentation of a domain specific language to describe the function and structure of hydraulic systems as well as a framework for synthesizing hydraulic systems using graph grammars to generate system topologies. Also, a method using graph grammars for generating analysis models from the described structural system representations is presented. This approach fits in the context of Model-Based Systems Engineering where a variety of formal models are used to represent knowledge about a system. It uses the Systems Modeling Language developed by The Object Management Group (OMG SysML™) as a unifying language for model definition.

In Artificial Intelligence, especially in area of constraint programming, it's popular to design various modeling languages which allow solving problems on domain level and by using domain specific abstractions. Techniques known from research on Domain-Specific Languages are often useful in this effort. Functional programming languages offer new tools for designing such languages, particularly Domain-Specific Embedded Languages. This work investigates the advantages and disadvantages of using functional programming for designing and implementing a Domain-Specific Embedded Language for state space search problems.