Declarative Language Research Articles

Parallel schedule synthesis for attribute grammars

We examine how to synthesize a parallel schedule of structured traversals over trees. In our system, programs are declaratively specified as attribute grammars. Our synthesizer automatically, correctly, and quickly schedules the attribute grammar as a composition of parallel tree traversals. Our downstream compiler optimizes for GPUs and multicore CPUs. We provide support for designing efficient schedules. First, we introduce a declarative language of schedules where programmers may constrain any part of the schedule and the synthesizer will complete and autotune the rest. Furthermore, the synthesizer answers debugging queries about how schedules may be completed. We evaluate our approach with two case studies. First, we created the first parallel schedule for a large fragment of CSS and report a 3X multicore speedup. Second, we created an interactive GPU-accelerated animation of over 100,000 nodes.

Forty hours of declarative programming: Teaching Prolog at the Junior College Utrecht

This paper documents our experience using declarative languages to give secondary school students a first taste of Computer Science. The course aims to teach students a bit about programming in Prolog, but also exposes them to important Computer Science concepts, such as unification or searching strategies. Using Haskell's Snap Framework in combination with our own NanoProlog library, we have developed a web application to teach this course.

Reducing duplication and redundancy in declarative model specifications

Event Abstract Back to Event Reducing duplication and redundancy in declarative model specifications Robert Cannon1*, Padraig Gleeson2, Sharon Crook3 and Angus Silver2 1 Textensor Limited, UK 2 University College London, United Kingdom 3 Arizona State University, United States Methods for storing and sharing biological models, whether by scripts, or with declarative languages such as SBML or CellML, tend to focus on directly encoding a model and its equations. However, many models share essentially the same equations, except for differences in parameter values or the number of instances of particular processes. We have therefore developed a mechanism within NeuroML[1] whereby the common structural and mathematical features of a family of models can be expressed separately from the parameter values that make up a particular member of the family. Specifications in this form correspond closely to the conceptual understanding that modelers have of the systems they work on. Supporting this style of description allows models to be expressed more concisely than if flatter structures are used since it avoids repeating common elements. It also enables a wide range of model related processing beyond simply executing them, including systematic comparisons, archiving, model composition, and sensitivity analysis. The resulting system, known as LEMS (Low Entropy Model Specification), has been developed to meet the needs of NeuroML but can also be applied to other declarative model specifications. It makes it possible to retro-fit most of the existing high level concepts ion NeuroML version 1 with domain independent definitions built out of the LEMS elements. This preserves the benefits of the existing semantically rich high-level concepts while adding the ability to extend it with new concepts at the user-level rather than requiring changes to the language. Simulator developers have a choice between directly supporting the library of core types in NeuroML or supporting the underlying LEMS definitions. Libraries are available in both Java and Python to allow simulator developers to work with LEMS models without having to implement the low level support themselves. These include options for flattening models into sets of simultaneous differential equations, and for code generation to create standalone executable units.

Automatic Generation of Test Cases in Regression Testing for Lustre/SCADE Programs

Lustre is a formal synchronous declarative language widely used for modeling and specifying safety critical applications in the fields of avionics, transportation, and energy production. In such applications, the testing activity to ensure correctness of the system plays a crucial role. During the development process, Lustre programs (or SCADE) are often upgraded, so regression test should be performed to detect bugs. However, regression test is generally costly, because the number of test cases is usually very large. In this paper, we present the solution to automatically generating test cases in regression testing of Lustre/SCADE programs. We apply this solution to regression testing for case study Uturn System.

GUBS, a Behaviour-Based Language for Design in Synthetic Biology

In this article, we propose a domain specific language, GUBS (Genomic Unified Behaviour Specification), dedicated to the behavioural specification of synthetic biological devices, viewed as discrete open dynamical systems. GUBS is a rule-based declarative language. In contrast to a closed system, a program is always a partial description of the behaviour of the system. The semantics of the language accounts the existence of some hidden non-specified actions that possibly alter the behaviour of the programmed devices. The compilation framework follows a scheme similar to automated theorem proving, aiming at improving synthetic biological design safety.

Scientific Cluster Deployment and Recovery – Using puppet to simplify cluster management

Deployment, maintenance and recovery of a scientific cluster, which has complex, specialized services, can be a time consuming task requiring the assistance of Linux system administrators, network engineers as well as domain experts. Universities and small institutions that have a part-time FTE with limited time for and knowledge of the administration of such clusters can be strained by such maintenance tasks.This current work is the result of an effort to maintain a data analysis cluster (DAC) with minimal effort by a local system administrator. The realized benefit is the scientist, who is the local system administrator, is able to focus on the data analysis instead of the intricacies of managing a cluster. Our work provides a cluster deployment and recovery process (CDRP) based on the puppet configuration engine allowing a part-time FTE to easily deploy and recover entire clusters with minimal effort.Puppet is a configuration management system (CMS) used widely in computing centers for the automatic management of resources. Domain experts use Puppet's declarative language to define reusable modules for service configuration and deployment.Our CDRP has three actors: domain experts, a cluster designer and a cluster manager. The domain experts first write the puppet modules for the cluster services. A cluster designer would then define a cluster. This includes the creation of cluster roles, mapping the services to those roles and determining the relationships between the services. Finally, a cluster manager would acquire the resources (machines, networking), enter the cluster input parameters (hostnames, IP addresses) and automatically generate deployment scripts used by puppet to configure it to act as a designated role. In the event of a machine failure, the originally generated deployment scripts along with puppet can be used to easily reconfigure a new machine.The cluster definition produced in our CDRP is an integral part of automating cluster deployment in a cloud environment. Our future cloud efforts will further build on this work.

Model Checking of Constraint-Based Workflow Based on Linear Temporal Logic

The degree of flexibility of workflow management systems heavily influences the way business processes are executed. Constraint-based models are considered to be more flexible than traditional models because of their semantics: everything that does not violate constraints is allowed. More and more people use declarative languages to define workflow, such as linear temporal logic. But how to guarantee the correctness of the model based on the linear temporal logic is still a problem. This article proposes a way to verify the model based on Büchi automaton and gives the corresponding algorithms. Thus the verification of declarative workflow based on the linear temporal logic is solved.

GUBS, a Behavior-based Language for Open System Dedicated to Synthetic Biology

In this article, we propose a domain specific language, GUBS (Genomic Unified Behavior Specification), dedicated to the behavioral specification of synthetic biological devices, viewed as discrete open dynamical systems. GUBS is a rule-based declarative language. By contrast to a closed system, a program is always a partial description of the behavior of the system. The semantics of the language accounts the existence of some hidden non-specified actions possibly altering the behavior of the programmed device. The compilation framework follows a scheme similar to automatic theorem proving, aiming at improving synthetic biological design safety.

Editorial: ASE 2012 conference trip report

– Models are becoming essential and integral to software engineering efforts, and often multiple types of models are used in engineering of a single product or line. Yet multiple models bring the added complexity of not only their individual correctness, but the correctness of the relationships among them. In “Automated verification of model transformations based on visual contracts”, Guerra, de Lara, Wimmer, Kappel, Kusel, Retschitzegger, Schonbock, and Schwinger provide a declarative language for specifying correctness contracts governing transformations and explore tool support for verifying them. – Automatically detecting and correcting code defects, especially those that lead to problems in the maintenance phase, has potential to significantly improve the economics of the software lifecycle by reducing the human time needed for this essential task. In “Maintainability defects detection and correction: a multi-objective approach”, Ouni, Kessentini, Sahraoui, and Boukadoum describe a search-based approach to automating this activity. – Refactoring of code is a critical and never-ending task in real world software development. One is constantly discovering new ways of modularizing, grouping, and generalizing the conceptual constructs and structural relationships in one’s code in order to better accommodate future change, clarify or improve correctness, and even to improve performance. One important type of refactoring is when

Semantics-aware data integration for heterogeneous data sources

This article presents a novel definition of a declarative mapping language, which is able to map precisely and unambiguously the semantics of a domain conceptualization (defined as an ontology) into queries to a set of data sources, where the data is residing. In this way, a system making use of this mapping language is able to access the data actually stored in the data sources thought a semantically rich representation. The mapping model proposed in this paper is also an ontology and therefore is machine understandable: it can be shared with other users or systems, processed by external tools for consistency checking, or collaboratively created and so on. Besides the contributions of the mapping model itself, this paper introduces the concepts of Semantic Join and Semantic Identifiers: a declarative approach to semantic data fusion and entity resolution over multiple unrelated databases, which allow to define extremely expressive mapping.

Malamud's “Subject Matter”:

Malamud's “Subject Matter”:

SP4

We propose the demonstration of SP4, a software-based programmable packet processing platform that supports (1) stateful packet processing useful for analyzing traffic flows with session semantics, (2) uses a task-stealing architecture that automatically leverages multi-core processing capabilities in a load-balanced manner without the need for explicit performance profiling, and (3) a declarative language for rapidly specifying and composing new packet processing functionalities from reusable modules. Our demonstration showcases the use of SP4 for performing high-throughput analysis of traffic traces for a variety of applications, such as filtering out unwanted traffic and detection of DDoS attacks using machine learning based analysis.

Entangled queries

Many data-driven social and Web applications involve collaboration and coordination. The vision of Declarative Data-Driven Coordination (D3C), proposed in Kot et al. [2010], is to support coordination in the spirit of data management: to make it data-centric and to specify it using convenient declarative languages. This article introduces entangled queries , a language that extends SQL by constraints that allow for the coordinated choice of result tuples across queries originating from different users or applications. It is nontrivial to define a declarative coordination formalism without arriving at the general (NP-complete) Constraint Satisfaction Problem from AI. In this article, we propose an efficiently enforceable syntactic safety condition that we argue is at the sweet spot where interesting declarative power meets applicability in large-scale data management systems and applications. The key computational problem of D3C is to match entangled queries to achieve coordination. We present an efficient matching algorithm which statically analyzes query workloads and merges coordinating entangled queries into compound SQL queries. These can be sent to a standard database system and return only coordinated results. We present the overall architecture of an implemented system that contains our evaluation algorithm. We also describe a proof-of-concept Facebook application we have built on top of this system to allow friends to coordinate flight plans. Finally, we evaluate the performance of the matching algorithm experimentally on realistic coordination workloads.

SAP Speaks PDDL: Exploiting a Software-Engineering Model for Planning in Business Process Management

Planning is concerned with the automated solution of action sequencing problems described in declarative languages giving the action preconditions and effects. One important application area for such technology is the creation of new processes in Business Process Management (BPM), which is essential in an ever more dynamic business environment. A major obstacle for the application of Planning in this area lies in the modeling. Obtaining a suitable model to plan with -- ideally a description in PDDL, the most commonly used planning language -- is often prohibitively complicated and/or costly. Our core observation in this work is that this problem can be ameliorated by leveraging synergies with model-based software development. Our application at SAP, one of the leading vendors of enterprise software, demonstrates that even one-to-one model re-use is possible. The model in question is called Status and Action Management (SAM). It describes the behavior of Business Objects (BO), i.e., large-scale data structures, at a level of abstraction corresponding to the language of business experts. SAM covers more than 400 kinds of BOs, each of which is described in terms of a set of status variables and how their values are required for, and affected by, processing steps (actions) that are atomic from a business perspective. SAM was developed by SAP as part of a major model-based software engineering effort. We show herein that one can use this same model for planning, thus obtaining a BPM planning application that incurs no modeling overhead at all. We compile SAM into a variant of PDDL, and adapt an off-the-shelf planner to solve this kind of problem. Thanks to the resulting technology, business experts may create new processes simply by specifying the desired behavior in terms of status variable value changes: effectively, by describing the process in their own language.

Ginga‐NCL architecture for plug‐ins

SUMMARYGinga is the middleware of the Japanese‐Brazilian terrestrial digital TV (DTV) system, ISDB‐T B, and also the ITU Telecommunication Standardization Sector (ITU‐T) Recommendation for Internet Protocol television services. Ginga supports NCL (Nested Context Language) applications in its declarative environment (Ginga‐NCL) for which we devise an architecture for plug‐in integration. This paper focuses on this plug‐in architecture and its innovative solutions, detailing the plug‐in API, life‐cycle and control model, besides discussing how the architecture can be extended to other software systems, in particular to other DTV middleware systems. In the paper, we show how Ginga‐NCL can embed other declarative language engines. On the other hand, we also extend an HTML browser to allow it to embed Ginga‐NCL. This proof‐of‐ concept is important because NCL and HTML players are not only the most common players found in DTV middleware systems but also because they are ITU Telecommunication Standardization Sector Recommendations for terrestrial and Internet Protocol television services (NCL and Broadcast Markup Language (BML)/Lightweight Interactive Multimedia Environment (LIME)). Therefore, this proof‐of‐concept also provides a contribution to DTV middleware interoperability. Copyright © 2012 John Wiley & Sons, Ltd.

Identifying Novel Drug Indications through Automated Reasoning

BackgroundWith the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.MethodologyIn this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.Conclusion/SignificanceTo evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.

RDOTE – Publishing Relational Databases into the Semantic Web

A necessary step for the evolution of the traditional Web into a Semantic Web is the transformation of the vast quantities of data, currently residing in Relational Databases into semantically aware data. In addition, in cases where new ontology schemata are developed, considerable experimentation with real data for testing the consistency of classes, properties and entailment rules is required. During the last decade, there has been intense research and development in creating methodologies and tools able to map Relational Databases with the Resource Description Framework (RDF). Although some systems have gained wider acceptance in the Semantic Web community, they either require users to learn a declarative language for encoding mappings or, in case they support friendly user interfaces, they provide limited expressivity.Thereupon, we present RDOTE, a framework for easily transporting data residing in Relational Databases into the Semantic Web. RDOTE is available under GNU/GPL license and it provides friendly graphical user interfaces, as well as enough expressivity for creating automatic and custom RDF dumps of relational data. RDOTE is also compatible with D2RQ and R2RML mapping definitions.

An inference engine toolkit for computing with words

Computing with Words is an emerging paradigm in knowledge representation and information processing. It provides a mathematical model to represent the meaning of imprecise words and phrases in natural language and introduces advanced techniques to perform reasoning on inexact knowledge. Since its introduction, there have been many studies on computing with words but mostly from the theoretical point of view and the paradigm still lacks sufficient support from the software side. This paper is an attempt to fill this gap by presenting an enhanced inference engine toolkit for supporting computing with words. The scope of the presented toolkit, as opposed to many available fuzzy logic tools, goes beyond simple fuzzy-if-then rules and performs a chain of inferences on complex fuzzy propositions containing fuzzy arithmetics, fuzzy quantifiers, and fuzzy probabilities. The toolkit may be appealing to researchers, practitioners, and educators in knowledge based applications and soft computing as it implements a powerful declarative language which allows users to express their knowledge in a more natural and convenient way and performs a chain of reasoning on imprecise propositions.

Exploring the functional implications of brain architecture and connectivity: a declarative language framework

We present a prototype framework for exploring hypotheses about the neuroanatomical structures and connectivity in the cerebellar cortex at various levels of granularity, based on experimental data and hypotheses from the scientific literature [1]. As illustrated in Figure ​Figure1,1, the framework consists of declarative and algorithmic components. The declarative components include languages for describing connectivity and neuronal and synaptic mechanisms, built as an extension to the NineML description language [2]. The algorithmic components are Python scripts and the PyNN program, which are used for interfacing to specific simulator platforms and for simulation control. Figure 1 The core assumptions of the framework are: 1) connectivity rules are specified as probability distributions for overlapping volumes of objects of different categories; 2) synapse locations are randomly generated from the distribution associated with an overlapping volume 3) the volumes that represent dendritic trees have regions of uniform synaptic density. We use the NineML language for declarative descriptions of integrate-and-fire neuronal dynamics, and we have built two extensions to NineML to describe conductance-based neuronal spiking mechanisms and geometric connectivity. The NineML Conductance language is an extension of NineML for describing Ohmic and GHK currents based on the Hodgkin-Huxley formalisms or Markov chains. The NineML BREP language is an extension of NineML for constructive 3D boundary representation [3] of neuroanatomical structures and connectivity at various levels of granularity (from coarse-resolution solids to fine meshes). NineML BREP is implemented on top of the GNU Triangulated Surface library [4], and provides the ability to specify geometric parameters for the instantiation of topological objects, such as coordinates for placement, or probability distributions for random placement of a group of identical objects; define categories of topological objects, such as stellate, basket and Golgi cells; define rules for connectivity between different categories of objects.

Declarative Policy-Based Adaptive Mobile Ad Hoc Networking

This paper presents DAWN, a declarative platform that creates highly adaptive policy-based mobile ad hoc network (MANET) protocols. DAWN leverages declarative networking techniques to achieve extensible routing and forwarding using declarative languages. We make the following contributions. First, we demonstrate that traditional MANET protocols can be expressed in a concise fashion as declarative networks and policy-driven adaptation can be specified in the same language to dictate the dynamic selection of different protocols based on various network and traffic conditions. Second, we propose interprotocol forwarding techniques that ensure packets are able to seamlessly traverse across clusters of nodes running different protocols selected based on their respective policies. Third, we have developed a full-fledged implementation of DAWN using the RapidNet declarative networking system. We experimentally validate a variety of policy-based adaptive MANETs in various dynamic settings using a combination of ns-3 simulations and deployment on the ORBIT testbed. Our experimental results demonstrate that hybrid protocols developed using DAWN outperform traditional MANET routing protocols and are able to flexibly and dynamically adapt their routing mechanisms to achieve a good tradeoff between bandwidth utilization and route quality. We further demonstrate DAWN's capabilities to achieve interprotocol forwarding across different protocols.