Declarative Specification Research Articles

Organic databases

Databases today are carefully engineered: there is an expensive and deliberate design process, after which a database schema is defined; during this design process, various possible instance examples and use cases are hypothesised and carefully analysed; finally, the schema is ready and then can be populated with data. All of this effort is a major barrier to database adoption. In this paper, we explore the possibility of organic database creation instead of the traditional engineered approach. The idea is to let the user start storing data in a database with a schema that is just enough to cove the instances at hand. We then support efficient schema evolution as new data instances arrive. By designing the database to evolve, we can sidestep the expensive front-end cost of carefully engineering the design of the database. Indeed, the deliberate design model complicates not only database creation, but also database transformation i.e., schema mapping because traditional schema mapping tasks are carefully engineered with declarative specification hidden beneath complex user interface. In this paper, we also study the issue of organic database transformation, which automatically induces schema mappings from sample target database instances.

SBVR to OWL 2 Mappings: An Automatable and Structural-Rooted Approach


 
 
 The wide applicability of mapping business rules expressions to ontology statements have been recently recognized. Some of the most important applications are: (1) using of on- tology reasoners to prove the consistency of business domain information, (2) generation of an ontology intended to be used in the analysis stage of a software development process, and (3) the possibility of encapsulate the declarative specification of business knowledge into information software systems by means of an implemented ontology. The Semantics of Business Vocabulary and Business Rules (SBVR) supports that approach by provid- ing business people with a linguistic way to semantically describe business concepts and specify business rules in an independent way of any information system design. Although previous work have presented some proposals, an exhaustive and automatable approach for them is still lacking. This work presents a broad and detailed set of transformations that allows the automatable generation of an ontology implemented in OWL 2 from the SBVR specifications of a business domain. Such transformations are rooted on the struc- tural specification of both standards and are depicted through a case study. A real case validation example was performed, approaching the feasibility of the mappings by the quality assessment of the developed ontology.
 
 

SODA: A framework for spatial observation data analysis

Very large amounts of geospatial data are daily generated by many observation processes in different application domains. The amount of produced data is increasing due to the advances in the use of modern automatic sensing devices and also in the facilities available to promote crowdsourcing data collection initiatives. Spatial observation data includes both data of conventional entities and also samplings over multi-dimensional spaces. Existing observation data management solutions lack declarative specification of spatio-temporal analytics. On the other hand, current data management technologies miss observation data semantics and fail to integrate the management of entities and samplings in a single data modeling solution. The present paper presents the design of a framework that enables spatio-temporal declarative analysis over large warehouses of observation data. It integrates the management of entities and samplings within a simple data model based on the well known mathematical concept of function. Observation data semantics are incorporated into the model with appropriate metadata structures.

SWOWS and dynamic queries to build browsing applications on linked data

The linked data initiative is pushing dataset maintainers to publish data online in a highly reusable way through a set of open standards, such as RDF and SPARQL. The variety and amount of structured data available on the Web are increasing, but their consumption is still quite limited. In particular, applications that are used to explore linked data are usually generic linked data browsers or applications with hard-coded logic tailored for specific needs. SWOWS is a platform for declarative specification of applications consuming linked data. In this paper, we describe the use of the platform for creating browsing applications tailored to specific contexts, and show how the declarative paradigm supports the development of flexible applications. To this end, the platform has been extended to support the dynamic generation of SPARQL queries. An example of a linked data browser created with the platform is given.

Bounded exhaustive test input generation from hybrid invariants

We present a novel technique for producing bounded exhaustive test suites from hybrid invariants, i.e., invariants that are expressed imperatively, declaratively, or as a combination of declarative and imperative predicates. Hybrid specifications are processed using known mechanisms for the imperative and declarative parts, but combined in a way that enables us to exploit information from the declarative side, such as tight bounds computed from the declarative specification, to improve the search both on the imperative and declarative sides. Moreover, our technique automatically evaluates different possible ways of processing the imperative side, and the alternative settings (imperative or declarative) for parts of the invariant available both declaratively and imperatively, to decide the most convenient invariant configuration with respect to efficiency in test generation. This is achieved by transcoping, i.e., by assessing the efficiency of the different alternatives on small scopes (where generation times are negligible), and then extrapolating the results to larger scopes. We also show experiments involving collection classes that support the effectiveness of our technique, by demonstrating that (i) bounded exhaustive suites can be computed from hybrid invariants significantly more efficiently than doing so using state-of-the-art purely imperative and purely declarative approaches, and (ii) our technique is able to automatically determine efficient hybrid invariants, in the sense that they lead to an efficient computation of bounded exhaustive suites, using transcoping.

Language Design with the Spoofax Language Workbench

IDEs are essential for programming language developers, and state-of-the-art IDE support is mandatory for programming languages to be successful. Although IDE features for mainstream programming languages are typically implemented manually, this often isn't feasible for programming languages that must be developed with significantly fewer resources. The Spoofax language workbench is a platform for developing textual programming languages with state-of-the-art IDE support. Spoofax is a comprehensive environment that integrates syntax definition, name binding, type analysis, program transformation, code generation, and declarative specification of IDE components. It also provides high-level languages for each of these aspects. These languages are highly declarative, abstracting over the implementation of IDE features and letting engineers focus on language design.

Breaking the chains

Data management research, systems, and technologies have drastically improved the availability of data analysis capabilities, particularly for non-experts, due in part to low-entry barriers and reduced ownership costs (e.g., for data management infrastructures and applications). Major reasons for the widespread success of database systems and today's multi-billion dollar data management market include data independence , separating physical representation and storage from the actual information, and declarative languages , separating the program specification from its intended execution environment. In contrast, today's big data solutions do not offer data independence and declarative specification. As a result, big data technologies are mostly employed in newly-established companies with IT-savvy employees or in large well-established companies with big IT departments. We argue that current big data solutions will continue to fall short of widespread adoption, due to usability problems, despite the fact that in-situ data analytics technologies achieve a good degree of schema independence. In particular, we consider the lack of a declarative specification to be a major road-block, contributing to the scarcity in available data scientists available and limiting the application of big data to the IT-savvy industries. In particular, data scientists currently have to spend a lot of time on tuning their data analysis programs for specific data characteristics and a specific execution environment. We believe that the research community needs to bring the powerful concepts of declarative specification to current data analysis systems, in order to achieve the broad big data technology adoption and effectively deliver the promise that novel big data technologies offer.

Generating optimized configurable business process models in scenarios subject to uncertainty

ContextThe quality of business process models (i.e., software artifacts that capture the relations between the organizational units of a business) is essential for enhancing the management of business processes. However, such modeling is typically carried out manually. This is already challenging and time consuming when (1) input uncertainty exists, (2) activities are related, and (3) resource allocation has to be considered. When including optimization requirements regarding flexibility and robustness it becomes even more complicated potentially resulting into non-optimized models, errors, and lack of flexibility. ObjectiveTo facilitate the human work and to improve the resulting models in scenarios subject to uncertainty, we propose a software-supported approach for automatically creating configurable business process models from declarative specifications considering all the aforementioned requirements. MethodFirst, the scenario is modeled through a declarative language which allows the analysts to specify its variability and uncertainty. Thereafter, a set of optimized enactment plans (each one representing a potential execution alternative) are generated from such a model considering the input uncertainty. Finally, to deal with this uncertainty during run-time, a flexible configurable business process model is created from these plans. ResultsTo validate the proposed approach, we conduct a case study based on a real business which is subject to uncertainty. Results indicate that our approach improves the actual performance of the business and that the generated models support most of the uncertainty inherent to the business. ConclusionsThe proposed approach automatically selects the best part of the variability of a declarative specification. Unlike existing approaches, our approach considers input uncertainty, the optimization of multiple objective functions, as well as the resource and the control-flow perspectives. However, our approach also presents a few limitations: (1) it is focused on the control-flow and the data perspective is only partially addressed and (2) model attributes need to be estimated.

Hybrid parallelization strategies for large-scale machine learning in SystemML

SystemML aims at declarative, large-scale machine learning (ML) on top of MapReduce, where high-level ML scripts with R-like syntax are compiled to programs of MR jobs. The declarative specification of ML algorithms enables---in contrast to existing large-scale machine learning libraries---automatic optimization. SystemML's primary focus is on data parallelism but many ML algorithms inherently exhibit opportunities for task parallelism as well. A major challenge is how to efficiently combine both types of parallelism for arbitrary ML scripts and workloads. In this paper, we present a systematic approach for combining task and data parallelism for large-scale machine learning on top of MapReduce. We employ a generic Parallel FOR construct (ParFOR) as known from high performance computing (HPC). Our core contributions are (1) complementary parallelization strategies for exploiting multi-core and cluster parallelism, as well as (2) a novel cost-based optimization framework for automatically creating optimal parallel execution plans. Experiments on a variety of use cases showed that this achieves both efficiency and scalability due to automatic adaptation to ad-hoc workloads and unknown data characteristics.

Framework for the declarative implementation of native mobile applications

The development of connected mobile applications for a broad audience is a complex task because of the existing device diversity. In order to soothe this situation, device-independent approaches are aimed at implementing platform-independent applications, hiding the differences among the diverse families and models of mobile devices. Most of the existing approaches are based on the imperative definition of applications, which are either compiled to a native application, or executed in a Web browser. The client and server sides of applications are implemented separately, using different mechanisms for data synchronisation. In this study, the authors propose device-independent mobile application generation (DIMAG), a framework for defining native device-independent client-server applications based on the declarative specification of application workflow, state and data synchronisation, user interface and data queries. The authors have designed DIMAG considering the dynamic addition of new types of devices, and facilitating the generation of applications for new target platforms. DIMAG has been implemented taking advantage of existing standards.

Translate the Stateflow Models into Alloy for Safety Analysis

Safety analysis is widely appreciated for critical systems. Model-based safety analysis is emerged under the background of mode-based development. For automated model-based safety analysis, extended models should be translated into script of input of formal analyzer. Consequently, many translation systems are constructed. In this paper, we explore the translating method between Stateflow models and Alloy, which is a declarative specification language for expressing complex structural constraints and behavior in a software system. Concrete translation systems could be built based on the method.

Understanding information update in questionnaires

Questionnaires are an important medium for collecting information in diverse areas of society (scientific surveys, tax filing, auditing guidance, etc.). We are interested in a domain-specific language (DSL) to automatically generate questionnaire software from declarative specifications. This note describes an important aspect of the semantics of such a DSL: what goes on when users fill out a form? The formalism is based on the epistemic notion of information update and has a wide range of applications. It provides a formal interpretation for query forms, and for the process of answering such forms. The attractiveness of the approach is in the fact that asking questions, providing partial answers to questions, and providing full answers to questions, are all modeled by the same mechanism of constraining a range of possibilities.

Business process verification with constraint temporal answer set programming

AbstractThe paper provides a framework for the verification of business processes, based on an extension of answer set programming (ASP) with temporal logic and constraints. The framework allows to capture expressive fluent annotations as well as data awareness in a uniform way. It allows for a declarative specification of a business process but also for encoding processes specified in conventional workflow languages. Verification of temporal properties of a business process, including verification of compliance to business rules, is performed by bounded model checking techniques in Answer Set Programming, extended with constraint solving for dealing with conditions on numeric data.

Generating sound and effective memory debuggers

We present a new approach for constructing debuggers based on declarative specification of bug conditions and root causes, and automatic generation of debugger code. We illustrate our approach on several classes of bugs, memory or otherwise. For each bug class, bug conditions and their root cause are specified declaratively, in First-order logic, using 1 to 4 predicates. We employ a low-level operational semantics and abstract traces to permit concise bug specification and prove soundness. To facilitate locating bugs, we introduce a new concept of value propagation chains that reduce programmer burden by narrowing the fault to a handful of executed instructions (1 to 16 in our experiments). We employ automatic translation to generate the debugger implementation, which runs on top of the Pin infrastructure. Experiments with using our system on 7 versions of 4 real-world programs show that our approach is expressive, effective at finding bugs and their causes, and efficient. We believe that, using our approach, other kinds of declaratively-specified, provably-correct, auto-generated debuggers can be constructed with little effort.

AUTOMATIC GENERATION OF OPTIMIZED BUSINESS PROCESS MODELS FROM CONSTRAINT-BASED SPECIFICATIONS

Business process (BP) models are usually defined manually by business analysts through imperative languages considering activity properties, constraints imposed on the relations between the activities as well as different performance objectives. Furthermore, allocating resources is an additional challenge since scheduling may significantly impact BP performance. Therefore, the manual specification of BP models can be very complex and time-consuming, potentially leading to non-optimized models or even errors. To overcome these problems, this work proposes the automatic generation of imperative optimized BP models from declarative specifications. The static part of these declarative specifications (i.e. control-flow and resource constraints) is expected to be useful on a long-term basis. This static part is complemented with information that is less stable and which is potentially unknown until starting the BP execution, i.e. estimates related to (1) number of process instances which are being executed within a particular timeframe, (2) activity durations, and (3) resource availabilities. Unlike conventional proposals, an imperative BP model optimizing a set of instances is created and deployed on a short-term basis. To provide for run-time flexibility the proposed approach additionally allows decisions to be deferred to run-time by using complex late-planning activities, and the imperative BP model to be dynamically adapted during run-time using replanning. To validate the proposed approach, different performance measures for a set of test models of varying complexity are analyzed. The results indicate that, despite the NP-hard complexity of the problems, a satisfactory number of suitable solutions can be produced.

RASP and ASP as a fragment of linear logic

RASP is a recent extension to Answer Set Programming (ASP) that permits declarative specification and reasoning on the consumption and production of resources. ASP can be seen as a particular case of RASP. In this paper, we study the relationship between linear logic and RASP problem specification. We prove that RASP programs can be translated into (a fragment of) linear logic, and vice versa. In doing so, we introduce a linear logic representation of default negation as understood in ASP. We are also able to establish a link between linear logic and here-and-there (HT) logic.

Chiron: a parallel engine for algebraic scientific workflows

SUMMARYLarge‐scale scientific experiments based on computer simulations are typically modeled as scientific workflows, which eases the chaining of different programs. These scientific workflows are defined, executed, and monitored by scientific workflow management systems (SWfMS). As these experiments manage large amounts of data, it becomes critical to execute them in high‐performance computing environments, such as clusters, grids, and clouds. However, few SWfMS provide parallel support. The ones that do so are usually labor‐intensive for workflow developers and have limited primitives to optimize workflow execution. To address these issues, we developed workflow algebra to specify and enable the optimization of parallel execution of scientific workflows. In this paper, we show how the workflow algebra is efficiently implemented in Chiron, an algebraic based parallel scientific workflow engine. Chiron has a unique native distributed provenance mechanism that enables runtime queries in a relational database. We developed two studies to evaluate the performance of our algebraic approach implemented in Chiron; the first study compares Chiron with different approaches, whereas the second one evaluates the scalability of Chiron. By analyzing the results, we conclude that Chiron is efficient in executing scientific workflows, with the benefits of declarative specification and runtime provenance support. Copyright © 2013 John Wiley & Sons, Ltd.

Principled parsing for indentation-sensitive languages

Several popular languages, such as Haskell, Python, and F#, use the indentation and layout of code as part of their syntax. Because context-free grammars cannot express the rules of indentation, parsers for these languages currently use ad hoc techniques to handle layout. These techniques tend to be low-level and operational in nature and forgo the advantages of more declarative specifications like context-free grammars. For example, they are often coded by hand instead of being generated by a parser generator. This paper presents a simple extension to context-free grammars that can express these layout rules, and derives GLR and LR(k) algorithms for parsing these grammars. These grammars are easy to write and can be parsed efficiently. Examples for several languages are presented, as are benchmarks showing the practical efficiency of these algorithms.

SitLog: A Programming Language for Service Robot Tasks

In this paper we present SitLog: a declarative situation-oriented logical language for programming situated service robot tasks. The formalism is task and domain independent, and can be used in a wide variety of settings. SitLog can also be seen as a behaviour engineering specification and interpretation formalism to support action selection by autonomous agents during the execution of complex tasks. The language combines the recursive transition network formalism, extended with functions to express dynamic and contextualized task structures, with a functional language to express control and content information. The SitLog interpreter is written in Prolog and SitLog's programs follow closely the Prolog notation, permitting the declarative specification and direct interpretation of complex applications in a modular and compact form. We discuss the structure and representation of service robot tasks in practical settings and how these can be expressed in SitLog. The present framework has been tested in the service robot Golem-II+ using the specification and programming of the typical tasks which require completion in the RoboCup@Home Competition.

Autonomous agents coordination: Action languages meet CLP() and Linda

AbstractThe paper presents a knowledge representation formalism, in the form of a high-levelAction Description Language (ADL)for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans. A distributed planning problem is formalized by providing the declarative specifications of the portion of the problem pertaining to a single agent. Each of these specifications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog.