One of the purposes of detecting the crisis related tweets is the ability to single out the tweets that provide information about the helps needed and offered. Classification of such tweets is difficult because of the unavailability of sufficient annotated tweets in those categories. To facilitate such classifications, a domain and event adaptive augmentation approach is proposed. The main objective

Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations

The research of traffic revitalization index can provide support for the formulation and adjustment of policies related to urban management, epidemic prevention and resumption of work and production. This paper proposes a deep model for the prediction of urban Traffic Revitalization Index (DeepTRI). The DeepTRI builds model for the data of COVID-19 epidemic and traffic revitalization index for major

Ontologies as a means to formally specify the knowledge of a domain of interest have made their way into information and communication technology. Most often, such knowledge is subject to continuous change, which demands for consistent evolution of ontologies and dependent artifacts. In this article, we study ontology evolution in the context of software security, where ontologies may be used to formalize

Document-oriented bases allow high flexibility in data representation which facilitates a rapid development of applications and enables many possibilities for data structuring. Unfortunately, in many cases, due to this flexibility and the absence of data modelling, the choice of a data representation is neglected by developers leading to many issues on several aspects of the document base and application

Process mining techniques help to improve processes using event data. Such data are widely available in information systems. However, they often contain highly sensitive information. For example, healthcare information systems record event data that can be utilized by process mining techniques to improve the treatment process, reduce patient’s waiting times, improve resource productivity, etc. However

In the field of Business Process Management (BPM), modeling business processes and related data is a critical issue since process activities need to manage data stored in databases. The connection between processes and data is usually handled at the implementation level, even if modeling both processes and data at the conceptual level should help designers in improving business process models and identifying

In many important subject domains, there are central real-world phenomena that span across multiple classification levels. In these subject domains, besides having the traditional type-level domain regularities (classes) that classify multiple concrete instances, we also have higher-order type-level regularities (metaclasses) that classify multiple instances that are themselves types. Multi-Level Modeling

Types are fundamental for conceptual modeling and knowledge representation, being an essential construct in all major modeling languages in these fields. Despite that, from an ontological and cognitive point of view, there has been a lack of theoretical support for precisely defining a consensual view on types. As a consequence, there has been a lack of precise methodological support for users when

Business process simulation is a versatile technique for quantitative analysis of business processes. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, various authors have proposed to discover simulation models from process

A single type of data store can hardly fulfill every end-user requirements in the NoSQL world. Therefore, polyglot systems use different types of NoSQL datastores in combination. However, the heterogeneity of the data storage models makes managing the metadata a complex task in such systems, with only a handful of research carried out to address this. In this paper, we propose a hypergraph-based approach

Software systems have an increasing value in our lives, as our society relies on them for the numerous services they provide. However, as our need for larger and more complex software systems grows, the risks involved in their operation also grows, with possible consequences in terms of significant material and social losses. The rational management of software defects and possible failures is a fundamental

Ontology-learning methods were introduced in the knowledge engineering area to automatically build ontologies from natural language texts related to a domain. Despite the initial appeal of these methods, automatically generated ontologies may have errors, inconsistencies, and a poor design quality, all of which must be manually fixed, in order to maintain the validity and usefulness of automated output

Many current applications and systems produce large tree datasets and export, exchange, and represent data in tree-structured form. Extracting informative patterns from large data trees is an important research direction with multiple applications in practice. Pattern mining research initially focused on mining induced patterns and gradually evolved into mining embedded patterns. A well-known problem

Nowadays, most enterprises collect, store, and manage personal information of customers to deliver their services. In such a setting, privacy has emerged as a key concern since companies often neglect or even misuse personal data. In response to multiple massive breaches of personal data, governments around the world have enacted laws and regulations for privacy protection. These laws dictate privacy

From a design science perspective, information systems and their components are viewed as artifacts. However, not much has been written yet on the ontological status of artifacts or their structure. After March & Smith’s (1995) initial classification of artifacts in terms of models, constructs, methods and instantiations, there have been only a few attempts to come up with a more systematic approach

The number and size of datasets abiding by the Linked Data paradigm increase every day. Discovering links between these datasets is thus central to achieving the vision behind the Data Web. Declarative Link Discovery (LD) frameworks rely on complex Link Specification (LS) to express the conditions under which two resources should be linked. Understanding such LS is not a trivial task for non-expert

Provenance in the context of workflows, both for their specification and for the data they derive, is essential for result reproducibility, sharing, and knowledge reuse in the scientific community. The information models to capture the provenance of scientific workflows, known as provenance models, have been of wide interest and studied in many fields, including the semantic Web. However, most existing

Clustering methods are generally used to study the homogeneity in a set of observations. The results obtained from the clustering process differ from one method to another, to the extent that the same method or validity index gives different outcomes depending on the initial parameters. Analytical evaluation appears to be insufficient for studying the behavior of clustering methods due to its ad hoc

Multidimensional modeling, i.e., the design of cube schemata, has a key role in data warehouse (DW) projects, in self-service business intelligence, and in general to let users analyze data via the OLAP paradigm. Though an effective involvement of users in multidimensional modeling is crucial in these projects, not much has been said about how to establish a fruitful collaboration in projects involving

Decision making is the lifeblood of the enterprise — from the mundane to the strategically critical. However, the increasing deluge of data makes it more important than ever to understand and use it effectively in every context. Being “data driven” is more aspiration than reality in most organizations due to the complexity, volume, variability and velocity of data streams from every customer and employee

The International Conference on Conceptual Modeling celebrated 40 years of existence at its 38th edition held in Salvador, Brazil, on 4–7 November 2019. As one of the most traditional and well-known conferences in the database area, it has its origins on the Entity-Relationship Model proposed by Peter P. Chen in 1975. To celebrate such an accomplishment, this article goes over the ER history from distinct

Handling data imperfection is a crucial issue in many application domains. This is particularly true when handling imperfect data inputs in applications for Alzheimer’s patients. In this paper we first propose a typology of imperfection for data entered by Alzheimer’s patients or their caregivers in the context of these applications (mainly due to the memory discordance caused by the disease). This

Argument mining consists in the automatic identification of argumentative structures in texts. In this work we leverage existing discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. We propose a new annotation schema and use it to augment a corpus of computational linguistics

This paper describes how knowledge bases can be represented in and reasoned with in natural logic. Natural logic is a regimented fragment of natural language possessing a well-defined logical semantics. As such, natural logic may be considered an attractive alternative among the various knowledge representation logics such as description logics. Our version of natural logic expands formal ontologies

Testing reachability in a graph gains substantial interest as an important operation in network analysis and graph mining. In its simplest form, a reachability query is defined by a pair of nodes (u, v) and a graph G, and detects if there is a path from u to v. This paper addresses a specific case of reachability on multi-labeled distributed graphs, where the query is parameterized by a set of source

Prediction of anomalous behavior in industrial assets based on sensor reading represents a key focus in modern business practice. As a matter of fact, forecast of forthcoming faults is crucial to implement predictive maintenance, i.e. maintenance decision making based on real time information from components and systems, which allows, among other benefits, to reduce maintenance cost, minimize downtime

Data Warehouse (DW) and OLAP systems are first citizens of Business Intelligence tools. They are widely used in the academic and industrial communities for numerous different fields of application. Despite the maturity of DW and OLAP systems, with the advent of Big Data, more and more sources of data are available, and warehousing this data can lead to important quality issues. In this work, we focus

Personalised analytics is a powerful technology that can be used to improve the career, lifestyle, and health of individuals by providing them with an in-depth analysis of their characteristics as compared to other people. Existing research has often focused on mining general patterns or clusters, but without the facility for customisation to an individual’s needs. It is challenging to adapt such approaches

Extraction, transformation, and loading (ETL) processes are used to extract data from internal and external sources of an organization, transform these data, and load them into a data warehouse. The Business Process Modeling and Notation (BPMN) has been proposed for expressing ETL processes at a conceptual level. A different approach is studied in this paper, where relational algebra (RA), extended

In this paper we propose an innovative architecture, called Mo.Re.Farming, for handling agricultural data in an integrated fashion and supporting decision making in the precision agriculture domain. This architecture is oriented to data analysis and is inspired by Business Intelligence 2.0 approaches. It is hybrid in that it couples traditional and big data technologies to integrate heterogeneous data

Discovery, specification and proper representation of various aspects of business knowledge plays crucial part in model-driven information systems engineering, especially when it comes to the early stages of systems development. Being among the most applicable and advanced features of model-driven development, model transformation could help improving one of the most time- and resource-consuming efforts

Searching the query log of a database system has a variety of applications. In a complex database, relevant queries in the log can serve as an initial example for query formulation, or may elucidate how to query the data in an optimized manner. Searching for queries that may cause a security or a privacy breach could be used to detect leaks of sensitive data. In general, queries in the query log can

Privacy-Preserving Record Linkage (PPRL) supports the integration of sensitive information from multiple datasets, in particular the privacy-preserving matching of records referring to the same entity. PPRL has gained much attention in many application areas, with the most prominent ones in the healthcare domain. PPRL techniques tackle this problem by conducting linkage on masked (encoded) values.

Conversational recommender system is a tool to help customer in deciding products they are going to buy, by conversational mechanism. By this mechanism, the system is able to imitate natural conversation between customer and professional sales support, for eliciting customer preference. However, many customers are not familiar with the technical features of multi-function and multi-feature products

Lower-dimensional representation of scientific text has attracted much attention among researchers due to its impact on many data mining and recommendation tasks. This paper studies two main research streams in scientific literature representation. First, both local and distributed representation viewpoints are reviewed and their advantages and disadvantages in lower dimensional representation are

There are numerous applications where users wish to identify subsets of vertices in a social network or graph database that are of interest to them. They may specify sets of patterns and vertex properties, and each of these confers a score to a subgraph. The users want to find the subgraphs with top-k highest scores. Examples in the real world where such subgraphs involve custom scoring methods include:

Skyline query has attracted a great deal of interest during last years because of its ability to help decision makers when multi-criteria objectives are to be handled. Several authors have pointed the interest of multidimensional skylines, i.e., the set of criteria become a parameter of the query. In order to efficiently evaluate these queries, index structures have been proposed. In this paper, we

The process of converting natural language specifications into conceptual models requires detailed analysis of natural language text, and designers frequently make mistakes when undertaking this transformation manually. Although many approaches have been used to partly automate this process, one of the main limitations is the lack of a domain-independent ontology that can be used as a repository for

Text similarity measures play a very important role in several text mining applications. Although there is an extensive literature on measuring the similarity between long texts, there is less work related to the measurement of similarity between short texts. And most of these works on short text similarity are based on adaptations of long-text similarity methods. Unfortunately, the description of

Heterogeneous information networks (HINs) composed of multiple types of nodes and links, play increasingly important roles in real life applications. Classification of the related data is an essential work in network analysis. Existing methods can effectively solve these classification tasks when they are applied to homogeneous information networks and simple data, but not for the noisy and sparse

Big data is progressively being used in various areas, such as industry, financial dealing, medicine, and so on, as it can handle the challenges in processing large amounts of data. One of the data mining techniques used widely and effectively to classify big data is the MapReduce model. In this paper, an approach for the classification of big data is developed using Cuckoo–Grey wolf based Correlative