Declarative Language Research Articles

Partov: a network simulation and emulation tool

Network protocol design and evaluation requires either full implementation of the considered protocol and evaluation in a real network, or a simulation based on a model. There is also a middle approach in which both simulation and emulation are used to evaluate a protocol. In this article the Partov engine, which provides both simulation and emulation capabilities simultaneously, is presented. Partov benefits from a layered and platform-independent architecture. As a pure simulator, it provides an extensible plugin-based platform that can be configured to perform both real-time and non-real-time discrete-event simulations. It also acts as an emulator, making interaction with real networks possible in real time. Additionally, a declarative XML-based language is used, acting as a glue between simulation and emulation modules and plugins. It supports dynamic network modelling and simulation based on continuous time Markov chains. Partov is compared with other well-known tools such as NS-3 and real processes such as Hping3. It is shown that Partov requires less overhead and is much more scalable than NS-3.

LDQL: A query language for the Web of Linked Data

The Web of Linked Data is composed of tons of RDF documents interlinked to each other forming a huge repository of distributed semantic data. Effectively querying this distributed data source is an important open problem in the Semantic Web area. In this paper, we propose LDQL, a declarative language to query Linked Data on the Web. One of the novelties of LDQL is that it expresses separately (i) patterns that describe the expected query result, and (ii) Web navigation paths that select the data sources to be used for computing the result. We present a formal syntax and semantics, prove equivalence rules, and study the expressiveness of the language. In particular, we show that LDQL is strictly more expressive than all the query formalisms that have been proposed previously for Linked Data on the Web. We also study some computability issues regarding LDQL. We first prove that when considering the Web of Linked Data as a fully accessible graph, the evaluation problem for LDQL can be solved in polynomial time. Nevertheless, when the limited data access capabilities of Web clients are considered, the scenario changes drastically; there are LDQL queries for which a complete execution is not possible in practice. We formally study this issue and provide a sufficient syntactic condition to avoid this problem; queries satisfying this condition are ensured to have a procedure to be effectively evaluated over the Web of Linked Data.

Extending multimedia languages to support multimodal user interactions

Historically, the Multimedia community research has focused on output modalities, through studies on timing and multimedia processing. The Multimodal Interaction community, on the other hand, has focused on user-generated modalities, through studies on Multimodal User Interfaces (MUI). In this paper, aiming to assist the development of multimedia applications with MUIs, we propose the integration of concepts from those two communities in a unique high-level programming framework. The framework integrates user modalities --both user-generated (e.g., speech, gestures) and user-consumed (e.g., audiovisual, haptic)-- in declarative programming languages for the specification of interactive multimedia applications. To illustrate our approach, we instantiate the framework in the NCL (Nested Context Language) multimedia language. NCL is the declarative language for developing interactive applications for Brazilian Digital TV and an ITU-T Recommendation for IPTV services. To help evaluate our approach, we discuss a usage scenario and implement it as an NCL application extended with the proposed multimodal features. Also, we compare the expressiveness of the multimodal NCL against existing multimedia and multimodal languages, for both input and output modalities.

Context-aware security: Linguistic mechanisms and static analysis

Adaptive systems improve their efficiency by modifying their behaviour to respond to changes in their operational environment. Also, security must adapt to these changes and policy enforcement becomes dependent on the dynamic contexts. We study these issues within MLCoDa, (the core of) an adaptive declarative language proposed recently. A main characteristic of MLCoDa is to have two components: a logical one for handling the context and a functional one for computing. We extend this language with security policies that are expressed in logical terms. They are of two different kinds: context and application policies. The first, unknown ap riorito an application, protect the context from unwanted changes. The others protect the applications from malicious actions of the context, can be nested and can be activated and deactivated according to their scope. An execution step can only occur if all the policies in force hold, under the control of an execution monitor. Beneficial to this is a type and effect system, which safely approximates the behaviour of an application, and a further static analysis, based on the computed effect. The last analysis can only be carried on at load time, when the execution context is known, and it enables us to efficiently enforce the security policies on the code execution, by instrumenting applications. The monitor is thus implemented within MLCoDa, and it is only activated on those policies that may be infringed, and switched off otherwise.

Computation (Abacus) Aspects of the Sahasralingam

India claims deep heritage in ancient sciences and specially in Mathematics & Astronomy – which means computation. Evidence based works not noted (archaeology platform). Sahasralingam (million indicator) is a artifact of indo archaeology. One devise from Bhubaneswar is studied from computation aspects. Has 1020 indentures which transpires as magic number. Sets of various types; Fibonacci; Pi; Permits continuous computations viz., calculus, algebra, discreet math in imperative and declarative languages; offers semantics; embedment; steganography & VLSI type; lattices of various orders; types & axis; symmetry with versatility and an extraordinary level of mathematical maturity is reported from the first time. 1020 posits also as unique & novel testing platform number.

From Datalog to flix: a declarative language for fixed points on lattices

We present Flix, a declarative programming language for specifying and solving least fixed point problems, particularly static program analyses. Flix is inspired by Datalog and extends it with lattices and monotone functions. Using Flix, implementors of static analyses can express a broader range of analyses than is currently possible in pure Datalog, while retaining its familiar rule-based syntax. We define a model-theoretic semantics of Flix as a natural extension of the Datalog semantics. This semantics captures the declarative meaning of Flix programs without imposing any specific evaluation strategy. An efficient strategy is semi-naive evaluation which we adapt for Flix. We have implemented a compiler and runtime for Flix, and used it to express several well-known static analyses, including the IFDS and IDE algorithms. The declarative nature of Flix clearly exposes the similarity between these two algorithms.

Declarative layout constraints for testing web applications

The paper focuses on bugs in web applications that can be detected by analyzing the contents and layout of page elements inside a browser's window. Based on an empirical analysis of 35 real-world web sites and applications (such as Facebook, Dropbox, and Moodle), it provides a survey and classification of more than 90 instances of layout-based bugs. It then introduces Cornipickle, an automated testing tool that provides a declarative language to express desirable properties of a web application as a set of human-readable assertions on the page's HTML and CSS data. Such properties can be verified on-the-fly as a user interacts with an application.

Adtpp: lightweight efficient safe polymorphic algebraic data types for C

SummaryAdtpp is an open‐source tool that adds support for algebraic data types (ADTs) to the C programming language. ADTs allow more precise description of program types and more robust handling of data structures than are directly supported by C. ADT definitions and other declarations are put in a file that is preprocessed by adtpp to produce a C header (‘.h’) file that can be included in C source files. The generated header file contains C type definitions, macros, and inline functions that support type‐safe construction, deconstruction, and pattern matching of ADT values while avoiding unsafe operations such as casts, and avoiding the risk of errors such as dereferencing NULL pointers and accessing inappropriate fields of unions. Values are represented efficiently, using techniques from the implementation of declarative languages. For many simple data types, the memory representation is identical to a direct implementation in C, with no loss of efficiency. For more complex types, the adtpp representation is more efficient than common C representations while preserving type safety and convenience. As an example, we present a new variation of 234‐trees that is very compact. Adtpp also supports parametric polymorphism such as defining a type ‘list of t’, where t can be any ADT, and generic functions such as length. However, polymorphic code is somewhat more verbose than for typical declarative languages, due to our reliance on the limited type checking available in C. Copyright © 2016 John Wiley & Sons, Ltd.

Statistical relational learning for workflow mining

The management of business processes can support efficiency improvements in organizations. One of the most interesting problems is the mining and representation of process models in a declarative language. Various recently proposed knowledge-based languages showed advantages over graph-based procedural notations. Moreover, rapid changes of the environment require organizations to check how compliant are new process instances with the deployed models. We present a Statistical Relational Learning approach to Workflow Mining that takes into account both flexibility and uncertainty in real environments. It performs automatic discovery of process models expressed in a probabilistic logic. It uses the existing DPML algorithm for extracting first-order logic constraints from process logs. The constraints are then translated into Markov Logic to learn their weights. Inference on the resulting Markov Logic model allows a probabilistic classification of test traces, by assigning them the probability of being compliant to the model. We applied this approach to three datasets and compared it with DPML alone, five Petri netand EPC-based process mining algorithms and Tilde. The technique is able to better classify new execution traces, showing higher accuracy and areas under the PR/ROC curves in most cases.

Domain-independent planning for services in uncertain and dynamic environments

Research in automated planning provides novel insights into service composition and contributes towards the provision of automatic compositions which adapt to changing user needs and environmental conditions. Most of the existing planning approaches to aggregating services, however, suffer from one or more of the following limitations: they are not domain-independent, cannot efficiently deal with numeric-valued variables, especially sensing outcomes or operator inputs, and they disregard recovery from runtime contingencies due to erroneous service behavior or exogenous events that interfere with plan execution. We present the RuGPlanner, which models the planning task as a Constraint Satisfaction Problem. In order to address the requirements put forward by service domains, the RuGPlanner is endowed with a number of special features. These include a knowledge-level representation to model uncertainty about the initial state and the outcome of sensing actions, and efficient handling of numeric-valued variables, inputs to actions or observational effects. In addition, it generates plans with a high level of parallelism, it supports a rich declarative language for expressing extended goals, and allows for continual plan revision to deal with sensing outputs, failures, long response times or timeouts, as well as the activities of external agents. The proposed planning framework is evaluated based on a number of scenarios to demonstrate its feasibility and efficiency in several planning domains and execution circumstances which reflect concerns from different service environments.

CyLog/Game aspect: An approach to separation of concerns in crowdsourced data management

In data-centric crowdsourcing, the output data are sensitive to the incentive structure connected to the workers׳ behavior. This paper proposes to use a declarative language to explicitly handle both data computation and the incentive structure. The language models computation as a set of Datalog-like rules, and the incentive structures for the crowd as games in which players׳ (workers׳) actions affect their received payoff. The language is unique in that it introduces the game aspect that separates the code for the incentive structure from the other logic encoded in the program. This paper shows that the game aspect not only enables easier analysis and maintenance of the incentive structures but also provides a principled model of the fusion of human and machine computations. In addition, we formally discuss how the rule-based language using the game concept integrates human and machine computations, and discuss its limitation and expressive power.

Declarative and Flexible Modeling of Software Product Line Architectures

Several known problems negatively affect the specification of software product lines architecture. One of them is related with a key concept known as Internal Variability. The internal variability of components is generally neglected when describing the architecture of a family of products. The impossibility to denote dynamic restrictions between features is also pointed out as a significant difficulty for achieving an architectural specification. A third problem arises when the high level abstractions needed at the architectural level are mixed with low-level artifacts as lines of code. This paper explores FVS (FeatherWeight Visual Scenarios) as a declarative language for specifying software product lines architectures in early stages of software development. FVS aims to take a step towards finding a solution to the mentioned problems.

CAT: A Cost-Aware Translator for SQL-query workflow to MapReduce jobflow

MapReduce is undoubtedly the most popular framework for large-scale processing and analysis of vast data sets in clusters of machines. To facilitate the easier use of MapReduce, SQL-like declarative languages and SQL-to-MapReduce translators have attracted increasing attentions recently. The SQL-to-MapReduce translator can automatically generate the MapReduce jobflow for each SQL query submitted by users, which significantly simplifies the interfacing between users and systems. Although a plethora of translators have been developed, the auto-generated MapReduce programs still suffered from extremely inefficiency. In this paper, we attempt to address this challenge by developing a novel Cost-Aware Translator (CAT). CAT has two notable features. First, it defines two intra-SQL correlations: Generalized Job Flow Correlation (GJFC) and Input Correlation (IC), based on which a set of looser merging rules are introduced. Thus, both Top-Down (TD) and Bottom-Up (BU) merging strategies are proposed and integrated into CAT simultaneously. Second, it adopts a cost estimation model for MapReduce jobflows to guide the selection of a more efficient MapReduce jobflows auto-generated by TD and BU merging strategies. Finally, comparative experiments on TPC-H benchmark demonstrate the effectiveness and scalability of CAT.

Declaratively building behavior by means of scenario clauses

Behavior needs to be understood from early stages of software development. In this context, incremental and declarative modeling seems an attractive approach for closely capturing and analyzing requirements without early operational commitment. A traditional choice for such a kind of modeling is a logic-based approach. Unfortunately, in many cases, the formal description and validation of properties result in a daunting task, even for trained people. Moreover, some authors established some practical limitations with temporal logics expressive power. In this work, we present omega-feather weight visual scenarios ($$\omega$$ź-FVS) a declarative language, not founded on temporal logics, but on simple graphical scenarios, powerful enough to express $$\omega$$ź-regular properties. The notation is equipped with declarative semantics based on morphisms, and a tableau procedure is given enabling the possibility of automatic analysis.

Performance Evaluation and Optimization of Multi-dimensional Indexes in Hive

Apache Hive has been widely used for big data processing over large scale clusters by many companies. It provides a declarative query language called HiveQL. The efficiency of filtering out query-irrelevant data from HDFS closely affects the performance of query processing. This is especially true for multi-dimensional, high-selective, and few columns involving queries, which provides sufficient information to reduce the amount of bytes read. Indexing (Compact Index, Aggregate Index, Bitmap Index, DGFIndex, and the index in ORC file) and columnar storage (RCFile, ORC file, and Parquet) are powerful techniques to achieve this. However, it is not trivial to choosing a suitable index and columnar storage based on data and query features. In this paper, we compare the data filtering performance of the above indexes with different columnar storage formats by conducting comprehensive experiments using uniform and skew TPC-H data sets and various multi-dimensional queries, and suggest the best practices of improving multi-dimensional queries in Hive under different conditions.

Integration of Big Data for Connected Cars Applications Based on Tethered Connectivity

The wireless communication technologies built-in or brought in the vehicle enable new in-car telematics services. The development of connected cars emphasizes the use of sophisticated computation framework for gathering, analyzing a large volume of data generated in all aspects of vehicle operations using Big Data technologies. Since these data are essential for many connected cars applications, the design and monitoring of MapReduce algorithms for processing vehicle's data using Hadoop framework will allow to build a hosting of analytics data source. This hosting data source allows different connected cars industry ecosystem to access useful data they need to afford connected cars applications.This paper studies design steps to take in consideration when implementing MapReduce patterns to analyze vehicle's data in order to produce accurate useful data that are hosted at the automakers and connect cars services providers. Experiment results show that MapReduce join algorithm is highly scalable and optimized for distributed computing than Statistical Analysis System (SAS) framework and HiveQL declarative language.

LDQL: A Query Language for the Web of Linked Data

The Web of Linked Data is composed of tons of RDF documents interlinked to each other forming a huge repository of distributed semantic data. Effectively querying this distributed data source is an important open problem in the Semantic Web area. In this paper, we propose LDQL, a declarative language to query Linked Data on the Web. One of the novelties of LDQL is that it expresses separately (i) patterns that describe the expected query result, and (ii) Web navigation paths that select the data sources to be used for computing the result. We present a formal syntax and semantics, prove equivalence rules, and study the expressiveness of the language. In particular, we show that LDQL is strictly more expressive than all the query formalisms that have been proposed previously for Linked Data on the Web. We also study some computability issues regarding LDQL. We first prove that when considering the Web of Linked Data as a fully accessible graph, the evaluation problem for LDQL can be solved in polynomial time. Nevertheless, when the limited data access capabilities of Web clients are considered, the scenario changes drastically; there are LDQL queries for which a complete execution is not possible in practice. We formally study this issue and provide a sufficient syntactic condition to avoid this problem; queries satisfying this condition are ensured to have a procedure to be effectively evaluated over the Web of Linked Data.

Hasselt

Implementing multimodal interactions with event-driven languages results in a ‘callback soup', a source code littered with a multitude of flags that have to be maintained in a self-consistent manner and across different event handlers. Prototyping multimodal interactions adds to the complexity and error sensitivity, since the program code has to be refined iteratively as developers explore different possibilities and solutions. The authors present a declarative language for rapid prototyping multimodal interactions: Hasselt permits declaring composite events, sets of events that are logically related because of the interaction they support, that can be easily bound to dedicated event handlers for separate interactions. The authors' approach allows the description of multimodal interactions at a higher level of abstraction than event languages, which saves developers from dealing with the typical ‘callback soup' thereby resulting in a gain in programming efficiency and a reduction in errors when writing event handling code. They compared Hasselt with using a traditional programming language with strong support for events in a study with 12 participants each having a solid background in software development. When performing equivalent modifications to a multimodal interaction, the use of Hasselt leads to higher completion rates, lower completion times, and less code testing than when using a mainstream event-driven language.

Lax Theory Morphisms

When relating formal languages, e.g., in logic or type theory, it is often important to establish representation theorems. These interpret one language in terms of another in a way that preserves semantic properties such as provability or typing. Metalanguages for stating representation theorems can be divided into two groups: First, computational languages are very expressive (usually Turing-complete), but verifying the representation theorems is very difficult (often prohibitively so); second, declarative languages are restricted to certain classes of representation theorems (often based on theory morphisms), for which correctness is decidable. Neither is satisfactory, and this article contributes to the investigation of the trade-off between these two methods. Concretely, we introduce lax theory morphisms, which combine some of the advantages of each: they are substantially more expressive than conventional theory morphisms, but they share many of the invariants that make theory morphisms easy to work with. Specifically, we introduce lax morphisms between theories of a dependently typed logical framework, but our approach and results carry over to most declarative metalanguages. We demonstrate the usefulness of lax theory morphisms by stating and verifying a type erasure translation from typed to untyped first-order logic. The translation is stated as a single lax theory morphism, and the invariants of the framework guarantee its correctness. This is the first time such a complex translation has be verified in a declarative framework.

A framework for programmatically designing user interfaces in JavaScript

Purpose – The purpose of this paper is to investigate the feasibility of creating a declarative user interface language suitable for rapid prototyping of mobile and Web apps. Moreover, this paper presents a new framework for creating responsive user interfaces using JavaScript. Design/methodology/approach – Very little existing research has been done in JavaScript-specific declarative user interface (UI) languages for mobile Web apps. This paper introduces a new framework, along with several case studies that create modern responsive designs programmatically. Findings – The fully implemented prototype verifies the feasibility of a JavaScript-based declarative user interface library. This paper demonstrates that existing solutions are unwieldy and cumbersome to dynamically create and adjust nodes within a visual syntax of program code. Originality/value – This paper presents the Guix.js platform, a declarative UI library for rapid development of Web-based mobile interfaces in JavaScript.