Unique Solutions in Data Exchange

Data exchange is the problem of transforming data structured under a source schema into data structured under a target schema in such a way that all constraints of a schema mapping are satisfied. At the heart of data exchange, lies a basic decision problem, called the existence-of-solutions problem: given a source instance, is there a target instance that satisfies the constraints of the schema mapping at hand? Earlier work showed that for schema mappings specified by embedded implicational dependencies, this problem is solvable in polynomial time, assuming that (1) the schema mapping is kept fixed and (2) the constraints of the schema mapping satisfy a certain structural condition, called weak acyclicity.We investigate the effect of these assumptions on the complexity of the existence-of-solutions problem, and show that each one is indispensable in deriving polynomial-time algorithms for this problem. Specifically, using machinery from universal algebra, we show that if the weak acyclicity assumption is relaxed even in a minimal way, then the existence-of-solutions problem becomes undecidable. We also show that if, in addition to the source instance, the schema mapping is part of the input, then the existence-of-solutions problem becomes EXPTIME-complete. Thus, there is a provable exponential gap between the data complexity and the combined complexity of data exchange. Finally, we study restricted classes of schema mappings and develop a comprehensive picture for the combined complexity of the existence-of-solutions problem for these restrictions. In particular, depending on the restriction considered, the combined complexity of this problem turns out to be either EXPTIME-complete or coNP-complete.

Flexible exchange of farming device data

Scene classification is usually based on supervised learning, but collecting instances is expensive and time-consuming. TrAdaBoost has achieved great success in transferring source instances to target images, but it has problems, such as the excessive focus on instances harder to classify, the rapid convergence speed of the source instances, and the weight mismatch caused by the big gap between the number of source and target instances, leading to decreased classification accuracy. In this paper, in order to address these problems, classical particle swarm optimization (PSO) is modified to select the optimal feature subspace for classifying the “harder” and “easier” instances by reducing unimportant dimensions. A modified correction factor is proposed by considering the classification accuracy of the instances from both domains, to decrease the convergence speed. Iterative selective TrAdaBoost is also proposed to reduce the weight mismatch by removing the indiscriminate source instances. The experimental results obtained with three benchmark data sets confirm that the proposed method outperforms most of the previous methods of scene classification with limited target samples. It is also proved that modified PSO for optimal feature subspace selection, the modified correction factor, and iterative selective TrAdaBoost are all effective in improving the classification accuracy, giving improvements of 3.6%, 4.3%, and 2.7%, and these three contributions together increase the classification accuracy by about 8% in total.

During the past two decades, data exchange has been explored in depth and in a variety of different settings. Even though temporal databases constitute a mature area of research studied over several decades, the investigation of temporal data exchange was initiated only very recently. We first present a chase algorithm for temporal data exchange settings with multiple temporal variables in the context of concrete time and show that this chase algorithm produces universal solutions, provided it does not fail. After this, we examine the relationship between universal solutions in the context of concrete time and universal solutions in the context of abstract time. We show that challenges arise even in the setting in which the data exchange specifications involve a single temporal variable, but also identify cases in which these challenges can be overcome.

A universal model of a database D and a set Σ of integrity constraints is a database that extends D, satisfies Σ, and is most general in the sense that it contains sound and complete information. Universal models have a number of applications including answering conjunctive queries, and deciding containment of conjunctive queries, with respect to databases with integrity constraints. Furthermore, they are used in slightly modified form as solutions in data exchange. In general, it is undecidable whether a database possesses a universal model, but in the past few years researchers identified various settings where this problem is decidable, and even efficiently solvable.This paper focuses on computing universal models under finite sets of guarded TGDs, non-conflicting keys, and negative constraints. Such constraints generalize inclusion dependencies, and were recently shown to be expressive enough to capture certain members of the DL-Lite family of description logics. The main result is an algorithm that, given a database without null values and a finite set Σ of such constraints, decides whether there is a universal model, and if so, outputs such a model. If Σ is fixed, the algorithm runs in polynomial time. The algorithm can be extended to cope with databases containing nulls; however, in this case, polynomial running time can be guaranteed only for databases with bounded block size.

This paper investigates a secure data exchange between many small distributed consumers/prosumers and the aggregator in the process of energy balancing. It addresses the challenges of ensuring data exchange in a simple, scalable, and affordable way. The communication platform for data exchange is using Ethereum Blockchain technology. It provides a distributed ledger database across a distributed network, supports simple connectivity for new stakeholders, and enables many small entities to contribute with their flexible energy to the system balancing. The architecture of a simulation/emulation environment provides a direct connection of a relational database to the Ethereum network, thus enabling dynamic data management. In addition, it extends security of the environment with security mechanisms of relational databases. Proof-of-concept setup with the simulation of system balancing processes, confirms the suitability of the solution for secure data exchange in the market, operation, and measurement area. For the most intensive and space-consuming measurement data exchange, we have investigated data aggregation to ensure performance optimisation of required computation and space usage.

Data exchange is the problem of transforming data structured under a schema (called source) into data structured under a different schema (called target). The emphasis of data exchange is to materialize a target instance (called solution) that satisfies the relationship between the schemas. Universal solutions were shown to be the most suitable solutions, mainly because they can be used to answer conjunctive queries posed over the target schema. Trying to extend this result to more expressive query languages fails, even if we only add inequalities (≠) to conjunctive queries.

Data exchange deals with inserting data from one database into another database having a different schema. Fagin et al. [2005] have shown that among the universal solutions of a solvable data exchange problem, there exists—up to isomorphism—a unique most compact one, “the core”, and have convincingly argued that this core should be the database to be materialized. They stated as an important open problem whether the core can be computed in polynomial time in the general setting where the mapping between the source and target schemas is given by source-to-target constraints that are arbitrary tuple generating dependencies (tgds) and target constraints consisting of equality generating dependencies (egds) and a weakly acyclic set of tgds. In this article, we solve this problem by developing new methods for efficiently computing the core of a universal solution. This positive result shows that data exchange based on cores is feasible and applicable in a very general setting. In addition to our main result, we use the method of hypertree decompositions to derive new algorithms and upper bounds for query containment checking and computing cores of arbitrary database instances. We also show that computing the core of a data exchange problem is fixed-parameter intractable with respect to a number of relevant parameters, and that computing cores is NP-complete if the rule bodies of target tgds are augmented by a special predicate that distinguishes a null value from a constant data value.

A Resource Reservation and Traffic Categorization Agent for QoS in Medical Ad Hoc Networks

This paper presents a schema mapping technique for XML-based geodata translation/transformation. XML is becoming the universal language for data exchange over the Internet. For geodata exchange, OGC defined the XML-based GML(Geographical Markup Language). Defining XML-based data format alone is not enough for data exchange. Associated techniques are equally important. The proposed schema mapping technique enables to a large extent automatic generation of geodata translator/convertor. Given a pair of source and target data schemas, a user specifies elements correspondence between them in a declarative way. The proposed schema mapping processor takes the schema mapping specification as input and generate geodata translator as output. Due to the automatic generation, semantic reconciliation between source and target schemas is very much facilitated.

XML schema mappings have been developed and studied in the context of XML data exchange, where a source document has to be restructured under the target schema according to certain rules. The rules are specified with a mapping, which consists of a set of source-to-target dependencies based on tree patterns. The problem of building a target document for a given source document and a mapping has polynomial data complexity, but is still intractable due to high combined complexity. We consider a two layer architecture for building target instances, inspired by the Church synthesis problem. We view the mapping as a specification of a document transformation, for which an implementation must be found. The static layer inputs a mapping and synthesizes a single XML-to-XML query implementing a valid transformation. The data layer amounts to evaluating this query on a given source document, which can be done by a specialized query engine, optimized to handle large documents. We show that for a given mapping one can synthesize a query expressed in an XQuery-like language, which can be evaluated in time proportional to the evaluation time of the patterns used in the mapping. In general the involved constant is high, but it can be improved under additional assumptions. In terms of overall complexity, if the arity of patterns is considered constant, we obtain a fixed-parameter tractable procedure with respect to the mapping size, which improves previously known upper bounds.

An End-to-End Secure Solution for IoMT Data Exchange

Device‐to‐device (D2D) and millimeter‐wave (mmW) communications play an important role in the design of future wireless communication systems since they are mature and resource‐efficient technologies. Integrating both technologies while retaining the benefit they offer in isolation is challenging. In this article, we review the literature on mmW‐based D2D proposals to enable network applications and service extensions that span from network‐controlled use cases to opportunistic solutions for direct data exchange, caching, and relay. The survey unveils that, although a large effort has been devoted to the study of D2D with mmW, we are still far from a full analytical and experimental characterization of the system. More effort is needed in view of 5G and beyond, to consider the integration of network computing elements and to protect mmW‐based D2D against security threats

A new minimum zone method for evaluating straightness errors

The task of combining data residing at different sources to provide the user a unified view is known as data integration. Schema mappings act as glue between the global schema and the source schemas of a data integration system. Global-and-local-as-view (GLAV) is one the approaches for specifying the schema mappings. Tableaux are used for expressing queries and functional dependencies on a single database. We investigate a general technique for expressing a GLAV mapping by a tabular structure called mapping assertion tableaux (MAT). In a similar way, we also express the tuple generating dependency (tgd) and equality generating dependency (egd) constraints by tabular forms, called tabular tgd (TTGD) and tabular egd (TEGD), respectively. A set consisting of the MATs, TTGDs and TEGDs are called schema mapping tableaux (SMT). We present algorithms that use SMT as operator on an instance of the source schema to produce an instance of the target schema. We show that the target instances computed by the SMT are `minimal' and `most general' in nature. We also define the notion of equivalence between the schema mappings of two data integration systems and present algorithms that optimize schema mappings through the manipulation of the SMT.