Dynamic Slicing for Concurrent Constraint Languages

Concurrent Constraint Programming (CCP) is a declarative model for concurrency where agents interact by telling and asking constraints (pieces of information) in a shared store. Some previous works have developed (approximated) declarative debuggers for CCP languages. However, the task of debugging concurrent programs remains difficult. In this paper we define a dynamic slicer for CCP (and other language variants) and we show it to be a useful companion tool for the existing debugging techniques. We start with a partial computation (a trace) that shows the presence of bugs. Often, the quantity of information in such a trace is overwhelming, and the user gets easily lost, since she cannot focus on the sources of the bugs. Our slicer allows for marking part of the state of the computation and assists the user to eliminate most of the redundant information in order to highlight the errors. We show that this technique can be tailored to several variants of CCP, such as the timed language ntcc, linear CCP (an extension of CCPbased on linear logic where constraints can be consumed) and some extensions of CCP dealing with epistemic and spatial information. We also develop a prototypical implementation freely available for making experiments.

Opportunities and challenges for deep constraint languages

Structural models are often augmented with additional well-formedness constraints to rule out unwanted configurations of instances. These constraints are usually written in dedicated constraint languages specifically tailored to the conceptual framework of the host modeling language, the most well-known example being the OCL constraint language for the UML. Many multi-level modeling languages, however, have no such associated constraint language. Simply adopting the OCL for such multi-level languages is not a complete strategy, though, as the OCL was designed to support the UML's two-level class/instance dichotomy, i.e., it can only define constraints which restrict the properties of the immediate instances of classes, but not beyond. The OCL would consequently not be able to support the definition of deep constraints that target remote or even multiple classification levels. In fact, no existing constraint language can address the full range of concerns that may occur in deep modeling using the Orthogonal Classification Architecture (OCA) as an infrastructure. In this paper we consider what these concerns might be and discuss the syntactical and pragmatic issues involved in providing full support for them in deep modeling environments.

Opportunities and challenges for deep constraint languages

Structural models are often augmented with additional well-formedness constraints to rule out unwanted configurations of instances. These constraints are usually written in dedicated constraint languages specifically tailored to the conceptual framework of the host modeling language, the most well-known example being the OCL constraint language for the UML. Many multi-level modeling languages, however, have no such associated constraint language. Simply adopting the OCL for such multi-level languages is not a complete strategy, though, as the OCL was designed to support the UML's two-level class/instance dichotomy, i.e., it can only define constraints which restrict the properties of the immediate instances of classes, but not beyond. The OCL would consequently not be able to support the definition of deep constraints that target remote or even multiple classification levels. In fact, no existing constraint language can address the full range of concerns that may occur in deep modeling using the Orthogonal Classification Architecture (OCA) as an infrastructure. In this paper we consider what these concerns might be and discuss the syntactical and pragmatic issues involved in providing full support for them in deep modeling environments.

Chain Length and CSPs Learnable with Few Queries

The goal of constraint acquisition is to learn exactly a constraint network given access to an oracle that answers truthfully certain types of queries. In this paper we focus on partial membership queries and initiate a systematic investigation of the learning complexity of constraint languages. First, we use the notion of chain length to show that a wide class of languages can be learned with as few as O(n log(n)) queries. Then, we combine this result with generic lower bounds to derive a dichotomy in the learning complexity of binary languages. Finally, we identify a class of ternary languages that eludes our framework and hints at new research directions.

Query Answering with Transitive and Linear-Ordered Data

We consider entailment problems involving powerful constraint languages such as frontier-guarded existential rules in which we impose additional semantic restrictions on a set of distinguished relations. We consider restricting a relation to be transitive, restricting a relation to be the transitive closure of another relation, and restricting a relation to be a linear order. We give some natural variants of guardedness that allow inference to be decidable in each case, and isolate the complexity of the corresponding decision problems. Finally we show that slight changes in these conditions lead to undecidability.

Directing the Development of Constraint Languages by Checking Constraints on RDF Data

For research institutes, data libraries, and data archives, validating RDF data according to predefined constraints is a much sought-after feature, particularly as this is taken for granted in the XML world. Based on our work in two international working groups on RDF validation and jointly identified requirements to formulate constraints and validate RDF data, we have published 81 types of constraints that are required by various stakeholders for data applications. In this paper, we evaluate the usability of identified constraint types for assessing RDF data quality by (1) collecting and classifying 115 constraints on vocabularies commonly used in the social, behavioral, and economic sciences, either from the vocabularies themselves or from domain experts, and (2) validating 15,694 data sets (4.26 billion triples) of research data against these constraints. We classify each constraint according to (1) the severity of occurring violations and (2) based on which types of constraint languages are able to express its constraint type. Based on the large-scale evaluation, we formulate several findings to direct the further development of constraint languages.