To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in

Augmented reality allows users to superimpose digital information (typically, of operational type) upon real-world objects. The synergy of analytical frameworks and augmented reality opens the door to a new wave of situated analytics, in which users within a physical environment are provided with immersive analyses of local contextual data. In this paper, we propose an approach named A-BI+ (Augmented

Technology development has accelerated the volume growth of complex data, such as images, videos, time series, and georeferenced data. Similarity search is a widely used approach to retrieve complex data, which aims at retrieving similar data according to intrinsic characteristics of the data. Therefore, to facilitate the retrieval of complex data using similarity searches, one needs to organize large

Similarity join is a key operation in metric databases. It retrieves all pairs of elements that are similar. Solving such a problem usually requires comparing every pair of objects of the datasets, even when indexing and ad hoc algorithms are used. We propose a simple and efficient algorithm for the computation of the approximated k nearest neighbor self-similarity join. This algorithm computes Θ(n3∕2)

COBIT 5 is a widely-used framework for implementing sound governance of enterprise IT (GEIT). Currently, the ISACA’s official implementation solution follows a sequentially ordered process, raising several issues related with lack of commitment from top management and misaligned solutions. Nevertheless, new project life-cycle strategies have emerged along with the agile paradigm for project management

We address the challenge of reconstructing historical information from aggregated, possibly inaccurate historical reports. For example, given a mixture of accurate and inaccurate monthly and weekly sums, how can we find accurately the daily counts of people infected with flu? We propose an approach, called A-Cure, that performs automatic data reconstruction from a combination of accurate and inaccurate

Approximate Nearest Neighbor (ANN) search is a prevalent paradigm for searching intrinsically high dimensional objects in large-scale data sets. Recently, the permutation-based approach for ANN has attracted a lot of interest due to its versatility in being used in the more general class of metric spaces. In this approach, the entire database is ranked by a permutation distance to the query. Typically

We define BitPart (Bitwise representations of binary Partitions), a novel exact search mechanism intended for use in high-dimensional spaces. In outline, a fixed set of reference objects is used to define a large set of regions within the original space, and each data item is characterised according to its containment within these regions. In contrast with other mechanisms only a subset of this information

Process modeling plays a central role in the development of today’s process-aware information systems both on the management level (e.g., providing input for requirements elicitation and fostering communication) and on the enactment level (providing a blue-print for process execution and enabling simulation). The literature comprises a variety of process modeling approaches proposing different modeling

It is a crucial need for a clustering technique to produce high-quality clusters from biomedical and gene expression datasets without requiring any user inputs. Therefore, in this paper we present a clustering technique called KUVClust that produces high-quality clusters when applied on biomedical and gene expression datasets without requiring any user inputs. The KUVClust algorithm uses three concepts

Recently, many methods have appeared in the field of cluster analysis. Most existing clustering algorithms have considerable limitations in dealing with local and nonlinear data patterns. Algorithms based on graphs provide good results for this problem. However, some widely used graph-based clustering methods, such as spectral clustering algorithms, are sensitive to noise and outliers. In this paper

Service-Oriented Architecture (SOA) has emerged as an architectural approach that enhances the service delivery performance of existing traditional systems while still retaining their most important features. This approach, due to its flexibility of adoption, has gained the attention of both academic and business entities, especially in the development of world-leading technologies such as Cloud Computing

The production of analytic datasets is a significant big data trend and has gone well beyond the scope of traditional IT-governed dataset development. Analytic datasets are now created by data scientists and data analysts using big data frameworks and agile data preparation tools. However, despite the profusion of available datasets, it remains quite difficult for a data analyst to start from a dataset

In malicious URLs detection, traditional classifiers are challenged because the data volume is huge, patterns are changing over time, and the correlations among features are complicated. Feature engineering plays an important role in addressing these problems. To better represent the underlying problem and improve the performance of classifiers in identifying malicious URLs, this paper proposed a combination

Similarity queries are fundamental operations for applications that deal with complex data. This paper presents MIA (Metric Indexing Assisted by auxiliary memory with limited capacity), a new delayed insertion approach that can be employed to create enhanced dynamic metric access methods through short-term memories. We present a comprehensive evaluation of delayed insertion methods for metric access

NeuroIS is a research field in which neuroscience theories and tools are used to better understand information systems phenomena. At present, NeuroIS is still an emerging field in information systems, and the number of available studies is limited. Because researchers who plan or execute NeuroIS research need to understand the status of the existing empirical research published in relevant journals

Motivated by the continuing interest in the tree data model, we study the expressive power of downward navigational query languages on trees and chains. Basic navigational queries are built from the identity relation and edge relations using composition and union. We study the effects on relative expressiveness when we add transitive closure, projections, coprojections, intersection, and difference;

Linked Data Wrappers (LDWs) turn Web APIs into RDF end-points, leveraging the Linked Open Data cloud with current data. Unfortunately, LDWs are fragile upon upgrades on the underlying APIs, compromising LDW stability. Hence, for API-based LDWs to become a sustainable foundation for the Web of Data, we should recognize LDW maintenance as a continuous effort that outlives their breakout projects. This

A popular approach to hosting Keyword Search Systems (KWS) on relational DBMS platforms is to employ the Candidate Network framework. The quality of a Candidate Network-based search is critically dependent on the scoring function used to rank the relevant answers. In this paper, we first demonstrate, through detailed empirical and conceptual analysis studies, that the Labrador scoring function provides

Positional ranking functions, widely used in web search engines and related search systems, improve result quality by exploiting the positions of the query terms within documents. However, it is well known that positional indexes demand large amounts of extra space, typically about three times the space of a basic nonpositional index. Textual data, on the other hand, is needed to produce text snippets

Despite the fact that JSON is currently one of the most popular formats for exchanging data on the Web, there are very few studies on this topic and there is no agreement upon a theoretical framework for dealing with JSON. Therefore in this paper we propose a formal data model for JSON documents and, based on the common features present in available systems using JSON, we define a lightweight query

Instance spanning constraints (ISC) are the instrument to establish controls across multiple instances of one or several processes. A multitude of applications crave for ISC support. Consider, for example, the bundling and unbundling of cargo across several instances of a logistics process or dependencies between examinations in different medical treatment processes. Non-compliance with ISC can lead

Exact set similarity join is a notoriously expensive operation, for which several solutions have been proposed. Recently, there have been studies that present a comparative analysis using MapReduce or a non-parallel setting. Our contribution is that we complement these works through conducting a thorough evaluation of the state-of-the-art GPU-enabled techniques. These techniques are highly diverse

Automated process discovery techniques enable users to generate business process models from event logs extracted from enterprise information systems. Traditional techniques in this field generate procedural process models (e.g., in the BPMN notation). When dealing with highly variable processes, the resulting procedural models are often too complex to be practically usable. An alternative approach

Memory devices can be used as storage systems to provide a lower latency that can be achieved by disk and flash storage. However, traditional buffered input/output (I/O) and direct I/O are not optimized for memory-based storages. Traditional buffered I/O includes a redundant memory copy with a disk cache. Traditional direct I/O does not support byte addressing. Memory-mapped direct I/O, which optimizes

Information systems, like biological systems, are susceptible to external perturbations. Similar to flora and fauna in a biome, species of data can be classified within a dataphora. While entropic properties and data geometries can be used to describe local species of data within a dataphora, they are not designed to describe the global properties of an information system or evaluate its stability

Data science requires constructing data processing pipelines (DPPs), which span diverse phases such as data integration, cleaning, pre-processing, and analysis. However, current solutions lack a strong data engineering perspective. As consequence, DPPs are error-prone, inefficient w.r.t. human efforts, and inefficient w.r.t. execution time. We claim that DPP design, development, testing, deployment

This paper presents a proposal aiming at better understanding a workload of SQL queries and detecting coherent explorations hidden within the workload. In particular, our work investigates SQLShare (Jain et al., 2016), a database-as-a-service platform targeting scientists and data scientists with minimal database experience, whose workload was made available to the research community. According to

Machine learning techniques play a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners. For a broader adoption and scalability of machine learning systems, the construction

Data preparation, whether for populating enterprise data warehouses or as a precursor to more exploratory analyses, is recognised as being laborious, and as a result is a barrier to cost-effective data analysis. Several steps that recur within data preparation pipelines are amenable to automation, but it seems important that automated decisions can be refined in the light of user feedback on data products

Process modeling has been widely investigated in the literature and several general purpose approaches have been introduced, addressing a variety of domains. However, generality goes to the detriment of the possibility to model details and peculiarities of a particular application domain. As acknowledged by the literature, known approaches predominantly focus on one aspect between control flow and

Data and software are nowadays one and the same: for this very reason, the European Union (EU) and other governments introduce frameworks for data protection — a key example being the General Data Protection Regulation (GDPR). However, GDPR compliance is not straightforward: its text is not written by software or information engineers but rather, by lawyers and policy-makers. As a design aid to information

The increasing adoption of modelling methods contributes to a better understanding of the flow of processes, from the internal behaviour of a single organisation to a wider perspective where several organisations exchange messages. In this regard, BPMN collaborations provide a suitable modelling abstraction. Even if this is a widely accepted notation, only a limited effort has been expended in formalising

Wireless Sensor Networks (WSNs) have become an integral part of urban scenarios. They are usually composed of a large number of devices. Developing systems for such networks is a hard task and often involves validation on simulation environments before deployment on real settings. Component-based development allows systems to be built from reusable, existing components that share a common interface

Given a process model and an event log, conformance checking aims to relate the two together, e.g. to detect discrepancies between them. For the synchronous product net of the process and a log trace, we can assign different costs to a synchronous move, and a move in the log or model. By computing a path through this (synchronous) product net, whilst minimizing the total cost, we create a so-called

In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a set of heuristics for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used

Information systems produce different types of event logs; in many situations, it may be desirable to look for trends inside these logs. We show how trends of various kinds can be computed over such logs in real time, using a generic framework called the trend distance workflow. Many common computations on event streams turn out to be special cases of this workflow, depending on how a handful of workflow

Service contracts bind parties legally, regulating their behavior in the scope of a (business) service relationship. Given that there are legal consequences attached to service contracts, understanding the elements of a contract is key to managing services in an enterprise. After all, provisions in a service contract and in legislation establish obligations and rights for service providers and customers

Contemporary process management systems support users during the execution of predefined business processes. However, when unforeseen situations occur, which are not part of the process model serving as the template for process execution, contemporary technology is often unable to offer adequate user support. One solution to this problem is to allow for ad-hoc changes to process models, i.e., changes

The understanding of the mobility on urban spaces is useful for the creation of smarter and sustainable cities. However, getting data about urban mobility is challenging, since only a few companies have access to accurate and updated data, that is also privacy-sensitive. In this work, we characterize three distinct car-sharing systems which operate in Vancouver (Canada) and nearby regions, gathering

Finding similar trajectories is an important task in moving object databases. However, classical similarity models face several limitations, including scalability and robustness. Recently, an approach named t2vec proposed transforming trajectories into points in a high dimensional vector space, and this transformation approximately keeps distances between trajectories. t2vec overcomes that scalability

Smart cities are nowadays equipped with pervasive networks of sensors that monitor traffic in real-time and record huge volumes of traffic data. These datasets constitute a rich source of information that can be used to extract knowledge useful for municipalities and citizens. In this paper we are interested in exploiting such data to estimate future speed in traffic sensor networks, as accurate predictions

Process mining aims at gaining insights into business processes by analyzing the event data that is generated and recorded during process execution. The vast majority of existing process mining techniques works offline, i.e. using static, historical data, stored in event logs. Recently, the notion of online process mining has emerged, in which techniques are applied on live event streams, i.e. as the

With the dynamic nature of cloud applications and rapid change of their resource requirements, elasticity over cloud resources has to be effectively supported. It represents the ability to dynamically adjust cloud resources that applications use in order to adapt to their varying workloads, while maintaining the desired quality of service. However, implementing elasticity is still challenging task

Distributed Ledger Technology (DLT) suggests a new way to implement Accounting Information Systems, but an ontologically sound consensus-based design is missing to date. Against this research gap, the paper introduces a DLT-based shared ledger solution in a formal way and compliant with Financial Reporting Standards. We build on the COFRIS accounting ontology (grounded on UFO) and the blockchain ontology

Detecting anomalies in process runtime behavior is crucial: they might reflect, on the one side, security breaches and fraudulent behavior and on the other side desired deviations due to, for example, exceptional conditions. Both scenarios yield valuable insights for process analysts and owners, but happen due to different reasons and require a different treatment. Hence a distinction into malign and

Most view-based modeling approaches are today based on a “synthetic” approach in which the views hold all the information modeled about a system and are kept consistent using explicit, inter-view correspondence rules. The alternative “projective” approach, in which the contents of views are “projected” from a single underlying model on demand, is far less widely used due to the lack of suitable conceptual

Enterprise Application Integration (EAI) constitutes the cornerstone in enterprise IT landscapes that are characterized by heterogeneity and distribution. Starting from established Enterprise Integration Patterns (EIPs) such as Content-based Router and Aggregator, EIP compositions are built to describe, implement, and execute integration scenarios. The EIPs and their compositions must be correct at

In recent years, monitoring the compliance of business processes with relevant regulations, constraints, and rules during runtime has evolved as major concern in literature and practice. Monitoring not only refers to continuously observing possible compliance violations, but also includes the ability to provide fine-grained feedback and to predict possible compliance violations in the future. The body

Enabling process changes constitutes a major challenge for any process-aware information system. This not only holds for processes running within a single enterprise, but also for collaborative scenarios involving distributed and autonomous partners. In particular, if one partner adapts its private process, the change might affect the processes of the other partners as well. Accordingly, it might have

Modeling collaboration processes is a challenging task. Existing modeling approaches are not capable of expressing the unpredictable, non-routine nature of human collaboration, which is influenced by the social context of involved collaborators. We propose a modeling approach which considers collaboration processes as the evolution of a network of collaborative documents along with a social network

We propose a dimensionality reduction technique for time series analysis that significantly improves the efficiency and accuracy of similarity searches. In contrast to piecewise constant approximation (PCA) techniques that approximate each time series with constant value segments, the proposed method--Piecewise Vector Quantized Approximation--uses the closest (based on a distance measure) codeword