ABSTRACT Process discovery is intended to machine‐learn process models by analysing event logs that record how real processes work. The resulting models tend to be complex because they must account for all the variability in the event logs. It is agreed that clustering the input event log prior to discovery may result in better models. Unfortunately, using a clustering approach has a negative computational impact on the time and the resources required to preprocess the event logs. Furthermore, the literature does not provide a fully developed study regarding their impact on the quality of the resulting process models. This makes it difficult for an expert to make informed decisions regarding how a particular event log must be clustered to reduce preprocessing effort and increase model quality. In this paper, we explore how to address the previous problem by means of a recommender that builds on a sound statistical procedure that is complemented with a visual analysis when necessary. Our proposal is intended to guide experts' work more efficiently and effectively by making informed decisions regarding how to cluster their event logs in process discovery.

Centralised architecture models are often adopted in the development of smart cities to take advantage of the technologies provided by dominant companies such as Google, Meta, and Amazon. This approach often comes with significant drawbacks, such as (i) application developers becoming dependent on the policies and rules imposed by large companies, which are subject to change, and (ii) users having their data collected and used haphazardly, and often without authorisation, which can undermine the trust that users place in the solution. Infrastructure for smart cities should, therefore, incorporate decentralised technologies that satisfy or reinforce important quality attributes such as trustworthiness, privacy, and security. The main objective of this article is to survey and discuss how the services of a smart city can be implemented using decentralised architectures; we explain the advantages and disadvantages of decentralised architectures and emerging technologies. We review the specialised literature and compile a set of emerging technologies that are essential to conceive a decentralised architectural solution for smart cities. The current state-of-the-art in decentralisation shows that this model can be implemented with current technologies and help to satisfy the quality attributes mentioned above, and potentially reinforce the degree of trustworthiness of the solution. We also propose a four-dimension guideline that functions as a conceptual driver for structuring actions for researchers, system architects, policymakers, and city authorities in the design, evaluation, and long-term maintenance of decentralised smart cities, thereby enhancing their capacity to address escalating urban challenges. We conclude that the set of technologies discussed in this article has the potential to be reused and replicated across smart cities. • Choice between C-architecture and D-architecture for smart cities. • Concerns about interoperability, privacy, vendor lock-in, and sustainability. • Decentralisation offers improved interoperability, privacy, and sustainability. • Approach to decentralised digital identity, permissions, and integration solutions.

Question answering aims at computing the answer to a question given a context with facts. Many proposals focus on questions whose answer is explicit in the context; lately, there has been an increasing interest in questions whose answer is not explicit and requires multi-hop inference to be computed. Our analysis of the literature reveals that there is a seminal proposal with increasingly complex follow-ups. Unfortunately, they were presented without an extensive study of their hyper-parameters, the experimental studies focused exclusively on English, and no statistical analysis to sustain the conclusions was ever performed. In this paper, we report on our experience devising a very simple neural approach to address the problem, on our extensive grid search over the space of hyper-parameters, on the results attained with English, Spanish, Hindi, and Portuguese, and sustain our conclusions with statistically sound analyses. Our findings prove that it is possible to beat many of the proposals in the literature with a very simple approach that was likely overlooked due to the difficulty to perform an extensive grid search, that the language does not have a statistically significant impact on the results, and that the empirical differences found among some existing proposals are not statistically significant.

Abstract Business rules govern how well‐managed companies perform every day. They are expected to be written in natural language because they are devised by business people. That makes it difficult to translate them automatically into executable rules that can be integrated into typical information systems. Our industrial and academic research concludes that the current tools have a number of problems, namely: many of them do not pay any attention to the SBVR standard; some of them are not multi‐language; most of them cannot achieve perfect parsing accuracy; some of them are not domain agnostic; some of them use proprietary technologies; some of them do not produce executable rules; and none supports exploratory what‐if analyses natively. In this article, we present Tier‐Rules, which is a system that overcomes the previous problems. We also report on an industrial case study that helps illustrate it in practice.

Abstract This article addresses major information systems integration problems, approaches, technologies, and tools within the context of Model‐Driven Software Engineering. The Guaraná integration platform is introduced as an innovative platform amongst state‐of‐the‐art technologies available for enterprises to design and implement integration solutions. In this article, we present its domain‐specific modeling language and its industrial cloud‐based web development platform, which supports the design and implementation of integration solutions. A real‐world case study is described and analyzed; then, we delve into its design and implementation, to finally disclose ten measures that empirically help estimating the amount of effort involved in the development of integration solutions.

Summary Aspect‐based sentiment analysis systems are a kind of text‐mining systems that specialize in summarizing the sentiment that a collection of reviews convey regarding some aspects of an item. There are many cases in which users write their reviews using conditional sentences; in such cases, mining the conditions so that they can be analyzed is very important not to misinterpret the corresponding sentiment summaries. Unfortunately, current commercial systems or research systems neglect conditions; current frameworks and toolkits do not provide any components to mine them; furthermore, the proposals in the literature are insufficient because they are based on handcrafted patterns that fall short regarding recall or machine learning procedures that are tightly bound with a specific language and require too much configuration. In this article, we present Torii, which is a system that loads a collection of reviews, discovers the aspects on which they report, and summarizes the sentiment that is conveyed on them taking into account the existing conditions, if any. We also describe its architecture, our approach to mine conditions, and our experimental analysis on a large multilingual data set with reviews from multiple categories. To the best of our knowledge, Torii is the first proposal that addresses aspect‐based sentiment analysis taking conditions into account.

Summary RDFa, JSON‐LD, Microdata, and Microformats allow to endow the data in HTML files with metadata tags that help software agents understand them. Unluckily, there are many HTML files that do not have any metadata tags, which has motivated many authors to work on proposals to synthesize them. But they have some problems: the authors either provide an overall picture of their designs without too many details on the techniques behind the scenes or focus on the techniques but do not describe the design of the software systems that support them; many of them cannot deal with data that are encoded using semistructured formats like forms, listings, or tables; and the few proposals that can work on tables can deal with horizontal listings only. In this article, we describe the design of a system that overcomes the previous limitations using a novel embedding approach that has proven to outperform four state‐of‐the‐art techniques on a repository with randomly selected HTML files from 40 different sites. According to our experimental analysis, our proposal can achieve an F1 score that outperforms the others by 10.14%; this difference was confirmed to be statistically significant at the standard confidence level.

A condition is a constraint that determines when something holds. Mining them is paramount to understanding many sentences properly. There are a few pattern-based approaches that fall short because the patterns must be handcrafted and it is not easy to characterise unusual ways to express conditions; there is one machine-learning approach that requires specific-purpose dictionaries, taxonomies, and heuristics, works on opinion sentences only, and was evaluated on a small dataset with Japanese sentences on hotels. In this paper, we present an encoder-decoder model to mine conditions that does not have any of the previous drawbacks and outperforms the state of the art in terms of effectiveness.

We are witnessing an increasing popularity of the Web of Data, which exposes a large variety of web sources that provide their data using RDF. Ontological models are used as the schema to organize this data. These models are usually shared by several communities and, to devise them, there is usually an agreement amongst those communities. As a result, it is common to have more than one ontological model to understand some RDF data; therefore, there might be a gap between the ontological models and the RDF data, which is not negligible in practice. In this article, the authors present a technique to automatically discover ontological models from raw RDF data. It is based on the intensive usage of a set of SPARQL 1.1 structural queries that are generic and independent from the RDF data. The final result of the authors' technique is an ontological model that is derived from the RDF data, and includes types and properties, subtypes, domains and ranges of properties and subproperties. The authors have conducted experiments with millions of triples that prove that their technique is suitable to deal with Big RDF Data. As far as they know, this is the first technique to discover such ontological models in the context of RDF data and the Web of Data.

Web data extractors are used to extract data from web documents in order to feed automated processes. In this article, we propose a technique that works on two or more web documents generated by the same server-side template and learns a regular expression that models it and can later be used to extract data from similar documents. The technique builds on the hypothesis that the template introduces some shared patterns that do not provide any relevant data and can thus be ignored. We have evaluated and compared our technique to others in the literature on a large collection of web documents; our results demonstrate that our proposal performs better than the others and that input errors do not have a negative impact on its effectiveness; furthermore, its efficiency can be easily boosted by means of a couple of parameters, without sacrificing its effectiveness.

The increasing popularity of the Web of Data is motivating the need to integrate semantic-web ontologies. Data exchange is one integration approach that aims to populate a target ontology using data that come from one or more source ontologies. Currently, there exist a variety of systems that are suitable to perform data exchange among these ontologies; unfortunately, they have uneven performance, which makes it appealing assessing and ranking them from an empirical point of view. In the bibliography, there exist a number of benchmarks, but they cannot be applied to this context because they are not suitable for testing semantic-web ontologies or they do not focus on data exchange problems. In this paper, we present MostoBM, a benchmark for testing data exchange systems in the context of such ontologies. It provides a catalogue of three real-world and seven synthetic data exchange patterns, which can be instantiated into a variety of scenarios using some parameters. These scenarios help to analyze how the performance of data exchange systems evolves as the exchanging ontologies are scaled in structured and/or data. Finally, we provide an evaluation methodology to compare data exchange systems side by side and to make informed and statistically sound decisions regarding: 1) which data exchange system performs better; and 2) how the performance of a system is influenced by the parameters of our benchmark.

Extracting information from web documents has become a research area in which new proposals sprout out year after year. This has motivated several researchers to work on surveys that attempt to provide an overall picture of the many existing proposals. Unfortunately, none of these surveys provide a complete picture, because they do not take region extractors into account. These tools are kind of preprocessors, because they help information extractors focus on the regions of a web document that contain relevant information. With the increasing complexity of web documents, region extractors are becoming a must to extract information from many websites. Beyond information extraction, region extractors have also found their way into information retrieval, focused web crawling, topic distillation, adaptive content delivery, mashups, and metasearch engines. In this paper, we survey the existing proposals regarding region extractors and compare them side by side.

Data translation, also known as data exchange, is an integration task that aims at populating a target model using data from a source model. This task is gaining importance in the context of semantic-web ontologies due to the increasing interest in graph databases and semantic-web agents. Currently, there are a variety of semantic-web technologies that can be used to implement data translation systems. This makes it difficult to assess them from an empirical point of view. In this paper, we present a benchmark that provides a catalogue of seven data translation patterns that can be instantiated by means of seven parameters. This allows us to create a variety of synthetic, domain-independent scenarios one can use to test existing data translation systems. We also illustrate how to analyse three such systems using our benchmark. The main benefit of our benchmark is that it allows to compare data translation systems side by side within a homogeneous framework.

The Cloud is evolving as a cost-effective solution to run services that support a variety of business processes. It is not surprising then that Orchestration as a Service (OaaS) is gaining importance as a means to integrate the many services a typical company runs or out sources in the Cloud. OaaS requires very efficient orchestration engines: the faster they work, the less customers have to pay and the more customers can be served. In this paper, we report on a new orchestration engine, we have performed a series of stringent experiments that prove that it outperforms a state-of-the-art orchestration engine in widespread use. Our conclusion is that our proposal is an efficient, solid orchestration engine ready for the Cloud.

Many authors are researching on information extraction techniques to transform the semi-structured information in typical web pages into structured information. When a researcher devises a new technique, he or she has to validate it, which requires implementing it, experimenting, gathering precision and recall results, comparing it to others, and drawing conclusions. This involves an array of details that are specific to this technique, but many others that are actually shared with other proposals. Unfortunately, the literature does not provide a single up-to-date platform to guide software engineers and researches in the design and implementation of information extractors. In this paper, we present a platform to design and implement learners of information extraction rules. Due to space constraints, we focus on the class of learners that learn hierarchical transducers. We have implemented our platform, and we have validated it by means of three case studies.

Enterprise Application Integration (EAI) solutions cope with two kinds of problems within software ecosystems, namely: keeping a number of application's data in synchrony or creating new functionality on top of them. Enterprise Service Bus (ESB) provides the technology required to implement a variety of EAI solutions at sensible costs, but they are still far from negligible. It is not surprising then that many authors are working on proposals to endow them with domain-specific tools to help software engineers reduce integration costs. In this article, we introduce a proposal called Guaraná. Its key features are as follows: it provides explicit support to devise EAI solutions using enterprise integration patterns by means of a graphical model; its DSL enables software engineers to have not only the view of a process, but also a view of the whole set of processes of which an EAI solution is composed; both processes and tasks can have multiple inputs and multiple outputs; and, finally, its runtime system provides a task-based execution model that is usually more efficient than the process-based execution models in current use. We have also implemented a graphical editor for our DSL and a set of scripts to transform our models into Java code ready to be compiled and executed. To set up a solution from this code, a software engineer only needs to configure a number of adapters to communicate with the applications being integrated.

One the most challenging problems for enterprise information integration is to deal with heterogeneous information sources on the Web. The reason is that they usually provide information that is in human-readable form only, which makes it difficult for a software agent to understand it. Current solutions build on the idea of annotating the information with semantics. If the information is unstructured, proposals such as S-CREAM, MnM, or Armadillo may be effective enough since they rely on using natural language processing techniques; furthermore, their accuracy can be improved by using redundant information on the Web, as C-PANKOW has proved recently. If the information is structured and closely related to a back-end database, deep annotation ranges among the most effective proposals, but it requires the information providers to modify their applications; if deep annotation is not applicable, the easiest solution consists of using a wrapper and transforming its output into annotations. In this paper, we prove that this transformation can be automated by means of an efficient, domain-independent algorithm. To the best of our knowledge, this is the first attempt to devise and formalize such a systematic, general solution

Abstract Multiparty interactions are a powerful mechanism for coordinating several entities that need to cooperate in order to achieve a common goal. In this paper, we present an algorithm for implementing them that improves on previous results in that it does not require the whole set of entities or interactions to be known at compile‐ or run‐time, and it can deal with both terminating and non‐terminating systems. We also present a comprehensive simulation analysis that shows how sensitive to changes our algorithm is, and compare the results with well‐known proposals by other authors. This study proves that our algorithm still performs comparably to other proposals in which the set of entities and interactions is known beforehand, but outperforms them in some situations that are clearly identified. In addition, these results prove that our algorithm can be combined with a technique called synchrony loosening without having an effect on efficiency. Copyright © 2004 John Wiley & Sons, Ltd.

The semantic Web shall enable Web agents an efficient, precise, and comprehensive extraction of knowledge. Nevertheless, this new Web is not likely to be adopted in the immediate future. We present a specification of a new framework in order to extract knowledge from today's dynamics nonsemantic Web. Our proposal is novel in that it associates semantics with the information extracted, which improves agent interoperability; it can also deal with changes to the structure of a Web page, which improves adaptability; furthermore, it achieves to delegate the knowledge extraction procedure to specialist agents, easing software development and promoting software reuse and maintainability.

Abstract In the context of the Multiparty Interaction Model, fairness is used to insure that an interaction that is enabled sufficiently often in a concurrent program will eventually be selected for execution. Unfortunately, this notion does not take conspiracies into account, i.e. situations in which an interaction never becomes enabled because of an unfortunate interleaving of independent actions; furthermore, eventual execution is usually too weak for practical purposes since this concept can only be used in the context of infinite executions. In this article, we present a new fairness notion, k‐conspiracy‐free fairness, that improves on others because it takes finite executions into account, alleviates conspiracies that are not inherent to a program, and k may be set a priori to control its goodness to address the above‐mentioned problems. Copyright © 2003 John Wiley & Sons, Ltd.

We present a light-weight approach for the automatic verification of requirements. This approach is not based on natural language parsing techniques but on the representation of requirements in XML. In our approach, XSLT stylesheets are used not only to automatically generate requirements documents, but also to provide verification-oriented heuristics as well as to measure the quality of requirements using some verification-oriented metrics. These ideas have been implemented in REM, an experimental XML-based requirements management tool also described.

This article reports on an error-repair algorithm for LR parsers. It locally inserts, deletes or shifts symbols at the positions where errors are detected, thus modifying the right context in order to resume parsing on a valid piece of input. This method improves on others in that it does not require the user to provide additional information about the repair process, it does not require precalculation of auxiliary tables, and it can be easily integrated into existing LR parser generators. A Yacc-based implementation is presented along with some experimental results and comparisons with other well-known methods.

Separation of concerns has been presented as a promising tool to tackle the design of complex systems in which cross-cutting properties that do no fit into the scope of a class must be satisfied. In this paper, we show that interaction amongst a number of objects can also be described separately from functionality by means of the CAL language, and present a framework that provides the needed infrastructure. It is innovative because it supports open multiparty interactions.