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In this paper, we evaluate the Lbl2Vec approach for unsupervised text document classification. Lbl2Vec requires only a small number of keywords describing the respective classes to create semantic label representations. For classification, Lbl2Vec uses cosine similarities between label and document representations, but no annotation information. We show that Lbl2Vec significantly outperforms common unsupervised text classification approaches and a widely used zero-shot text classification approach. Furthermore, we show that using more precise keywords can significantly improve the classification results of similarity-based text classification approaches.

Aha moment is when users realize the value of using the software product, which is a key to driving revenue, mainly for B2B SaaS vendors. According to the Acquisition-Activation-Retention-Referral-Revenue (AARRR) model, the aha moment can refer to activation, and the following customer phase is retention. This research aims to find customers’ ”aha moment” through process mining. Since the customers in retention are obligated to experience activation before, the research first identifies milestone actions in terms of product features and user roles to cluster the retention customers. Evaluation is performed through the data of the clustered customers from the time before they move to the retention phase. The event log analysis is discussed based on multiple dimensions: product solution, time, and user roles. The research mainly applies the process mining technique, heuristic miner, to discover the customer’s behavior patterns. Apart from marketing funnels, Moreover concept of human-computer interaction is focused on event classification and data cleaning, which is practical for cleaning UI logs. The discovered processes and aha moment can guide future product development and value proposition re-stratigizing.

Enterprises always do their business within some restrictions. In a team of enterprise architects, the restrictions are transformed into the modelling conventions and the corresponding modelling constraints that should be consistently applied across all enterprise models. This paper presents an approach for refining and formali\-zing modeling conventions into modelling constraints and using them for assessment of enterprise models by a software component called ArchiChecker. The specifics of the proposed approach is that the modeling conventions are first visualized and formalized using the types of elements and relationships of the ArchiMate modeling language, that is also used for modelling of enterprise views. The ArchiMate elements and relationships serve as types to formulate constraints. The elements and relationships in an ArchiMate model are instances of the ArchiMate elements and relationships. Using these types and instances the ArchiChecker automatically generates the lists of violations of modeling conventions in the enterprise models. Each violation shows how a specific enterprise view deviates from a given modeling convention. The paper reports a case study of application of the proposed approach to enterprise modelling views and modelling conventions used in a medical center. The case study is used to discuss the added value of formalization and automated assessment of modelling constraints in enterprise modelling.

CRM platforms heavily depend on high-quality data, where poor-quality data can negatively influence its adoption. Additionally, these platforms are increasingly interconnected and complex to meet growing needs of customers. Hence, delivery and consultancy of CRM platforms becomes highly complex. In this study, we propose a CRM data quality management framework that supports CRM delivery and consultancy firms to improve data quality management practices within their projects. The framework should also improve data quality within CRM solutions for their clients. We extract best practices for CRM data quality management by means of a literature study on data quality definition and measurement, data quality challenges, and data quality management methods. In a case study at an IT consultancy company, we investigate how CRM delivery and consultancy projects can benefit from the incorporation of data quality management practices. The design of the framework is validated by means of confirmatory focus groups and a questionnaire. The results translate into a framework that provides a high-level overview of data quality management practices incorporated in CRM delivery and consultancy projects. It includes the following components: Client profiling, project definition, preparation, migration/integration, data quality definition, assessment, and improvement.

Process mining derives knowledge of the execution of processes through analyzing behavior as observed from real-life events. While benefits of process mining are widely acknowledged, finding an adequate level of detail at which a mined process model is suitable for a specific stakeholder is still an ongoing challenge. Process models can be mined at different levels of abstraction, often resulting in either highly complex or highly abstract process models. This may have an important impact on the comprehensibility of the process model, which can also differ from the perspective of a particular stakeholder. To address this problem from a stakeholder-centric perspective, we propose a methodology for determining an appropriate level of process model abstraction. To this end, we use quantitative metrics on process models as well as a qualitative evaluation by using a technology acceptance model (TAM). A logistics case study involving the fuzzy process mining discovery algorithm shows init ial evidence that the use of appropriate abstraction levels is key when considering the needs of various stakeholders.

To measure Data Governance, researchers designed a few Data Governance Maturity Models, but the used capabilities are very different, resulting in different measurement outcomes. This research aims to find a substantiated and validated set of Data Governance capabilities for Data Governance Maturity Models design. We apply the Maturity Model design procedure model of Becker et al. for methodology, which we complement with the Generic Capability Reference model to validate the capabilities. As results, we find a proper set of Data Governance capabilities for designing a Data Governance Maturity Models. Furthermore, we validate the set of DG capabilities against the GCR model, of which we conclude that the Generic Capability Reference model is valid as a reference model for the (re)design of Maturity Models.

Scientific datasets are increasingly stored, published, and re-used online. This has prompted major search engines to start services dedicated to finding research datasets online. However, to date such services are limited to keyword search, and provide little or no semantic guidance. Determining the scientific domain for a given dataset is a crucial part in dataset recommendation and search: "Which research domain does this dataset belong to?". In this paper we investigate and compare a number of novel ontology-based methods to answer that question, using the distance between a domain-ontology and a dataset as an estimator for the domain(s) into which the dataset should be classified. We also define a simple keyword-based classifier based on the Normalized Google Distance, and we evaluate all classifiers on a hand-constructed gold standard. Our two main findings are that the seemingly simple task of determining the domain(s) of a dataset is surprisingly much harder than expected (even when performed under highly simplified circumstances), and that (again surprisingly), the use of ontologies seems to be of little help in this task, with the simple keyword-based classifier outperforming every ontology-based classifier. We constructed a gold-standard benchmark for our experiments which we make available online for others to use.

As knowledge graphs are getting increasingly adopted, the question of how to maintain the validity and accuracy of our knowledge becomes ever more relevant. We introduce context-aware constraints as a means to help preserve knowledge integrity. Context-aware constraints offer a more fine-grained control of the domain onto which we impose restrictions. We also introduce a bottom-up anytime algorithm to discover contextaware constraint directly from heterogeneous knowledge graphs-graphs made up from entities and literals of various (data) types which are linked using various relations. Our method is embarrassingly parallel and can exploit prior knowledge in the form of schemas to reduce computation time. We demonstrate our method on three different datasets and evaluate its effectiveness by letting experts on knowledge validation and management assess candidate constraints in a real-world knowledge validation use case. Our results show that overall, context-aware constraints are to an extent useful for knowledge validation tasks, and that the majority of the generated constraints are well balanced with respect to complexity.

Entity resolution in databases focuses on detecting and merging entities that refer to the same real-world object. Collective resolution is among the most prominent mechanisms suggested to address this challenge since the resolution decisions are not made independently, but are based on the available relationships within the data. In this paper, we introduce a novel resolution approach that combines the essence of collective resolution with rules and transformations among entity attributes and values. We illustrate how the approach’s parameters are optimized based on a global optimization algorithm, i.e., simulated annealing, and explain how this optimization is performed using a small training set. The quality of the approach is verified through an extensive experimental evaluation with 40M real-world scientific entities from the Patstat database.

Organizations across the world today face similar problems, thus, solutions to these problems could also be re-used across many organizations. Climate change is one such common problem to organizations, where any solution implemented by one could be re-used by many others. These re-usable solutions could be Enterprise Architecture Patterns, but there is a lack of guidance on how to use them in practice. This study proposes an extension to the commonly used TOGAF, to better leverage these reusable solutions. Thus, this enables organizations using this framework to enhance it with Enterprise Architecture Patterns. As part of the proposed methodology, re-usable patterns supporting sustainable characteristics are built. The resulting methodology is validated with an expert panel, gathering positive comments.

Chaos emerges with the ever growing amounts of data and information within organisations. But it is problematic to manage these valuable assets and also remain accountable and compliant for them because there is no agreement about even their definitions. Our objective is to propose a coherent set of definitions for data governance and information governance within and across organisations in relation with data and information as underlying concepts. As a research method, we explore elements from existing definitions in literature about the Data-Information-Knowledge-Wisdom pyramid and about data governance and information governance. Classification of these elements and coding them in concepts during discussions among peers resulted in a new vocabulary. This forms the basis for formulation and design of an original coherent set of definitions for data, information, meaning, data governance and information governance. This research is grounded, goal oriented and uses multiple accepted literature review methods. But it is limited to the literature found and the IS domain.

Organizations around the world today face many challenges, and not all of them are unique. Such repeatable problems can themselves be solved by reusable solutions. This study focuses on Enterprise Architecture Patterns, reusable solutions to repeating challenges organizations face. Through a systematic literature review, we aim to gather an exhaustive recollection of all such patterns in literature. In order to be as comprehensive as possible, the sample included related fields also researching patterns. The result is a collection of 593 patterns and their respective analysis, which will help practitioners tackle their challenges.

We propose in this paper a conceptual framework for the hierarchical decomposition of Non-Functional Requirements (NFRs) from the business process level to the web service level. This framework seeks to reduce the dependence on a particular IT expert’s knowledge by simplifying the dialog between the business and IT areas. The proposed framework relies on a structure of NFRs interdependence. The main reference was the ISO/IEC 25010 Product Quality Model, extended by additional software quality models and particular QoS attributes. This framework is accompanied by an extensive dictionary of non-functional requirements for both business processes and web services that can serve as a reference for researchers and industry practitioners. We assume that orchestrating web services to run business processes requires a rigorous definition of the functional requirements and NFRs of these web services. Web service NFRs are often defined as Quality of Service (QoS) attributes, which is done at the implementation level by IT teams. The definition of QoS attributes should consider the business process NFRs, since misinterpretations of web service NFRs may affect the behavior of the web services and hence achieving the business goals. The approaches proposed so far in the literature are still heavily dependent on an IT expert’s knowledge to identify the appropriate QoS attributes required to meet particular business process NFRs. However, defining appropriate QoS attributes without reference to business process-level NFRs may be a costly, time-consuming task.

The analysis of a historical document of a registry office from nineteenth century through Raman spectroscopy was performed by comparing with an ensemble of standard samples synthesized from cotton and linen raps. Structural parameters of the fibers of the cellulose, such as crystallinity, chain length, intermolecular interactions and packing, were obtained by the measurement of intensity ratios of some marker features. The results suggested the historical document has a cellulosic support originated from cotton, probably produced without basic pH treatment of the raw material.

Business monitoring approaches usually address indicators associated with processes only at the service level; i.e., related to the services implementing the processes. Monitoring at the service level raises technical measures geared to Information Technology (IT) managers. Monitoring of Key Performance Indicators (KPIs) is usually carried out at a higher level, but transversely to the organization’s processes, i.e., uncoupled from the processes. We present a component designed to aid in strategic alignment between business and IT by monitoring Non-Functional Requirements (NFR) of processes based on Quality of Service attributes. This component aims to allow business managers to monitor process executions by focusing on the indicators that truly respond to the execution of such processes. We evaluated the component via a proof of concept.

Non-functional Requirements (NFRs) of web services are defined by IT teams at the implementation level often as Quality of Service (QoS) attributes. Orchestrating web services to run business processes requires a rigorous definition of the NFRs of such web services. The definition of QoS attributes should consider the business process NFRs since misinterpretations of web service NFRs may affect the behavior of the web services and hence achieving the business goals. The approaches proposed so far are still heavily dependent on an IT expert’s knowledge to identify the appropriate QoS attributes required to meet particular business process NFRs. Defining appropriate QoS attributes without reference to business process-level NFRs may be a costly, time-consuming task. We propose a conceptual framework for the hierarchical decomposition of NFRs from the business process level to the web service level. This framework seeks to reduce the dependence on a particular IT expert’s knowledge by simplifying the dialog between the business and IT areas. The proposed framework relies on a structure of NFRs interdependence. The main reference was the ISO/IEC 25010 Product Quality Model, extended by additional software quality models and particular QoS attributes.

Genipa americana, pertencente à família Rubiaceae, árvore de grande porte que é encontrada em várias áreas do Brasil, popularmente conhecida como Jenipapeiro sendo utilizada para no combate à doenças hepáticas e inflamatórias, além de reduzir o colesterol. Este estudo tem como principal objetivo avaliar a prospecção fitoquímica, antibacteriana e modulatória do extrato hidroalcoólico da casca do caule de Genipa americana frente a cepas de bactérias padrões e multirresistentes. Para a análise da atividade antibacteriana do extrato hidroalcoólico, foi realizado o teste de microdiluição em caldo para determinação da concentração inibitória mínima (CIM), e a modulação de antimicrobianos por meio de gentamicina, amicacina e clindamicina. Os resultados obtidos da CIM pelas bactérias Escherichia coli, Staphylococcus aureus e Pseudomonas aeruginosa foram ≥ 1024µg/mL. Na modulação modificação da concentração de dois antibióticos para cada cepa bacteriana. Assim, é fato rematar que este extrato tem notável atividade moduladora principalmente quando testados contra bactérias de interesse clínico como a P. aeruginosa, bactérias oportunista, ressaltando que deve-se ser realizados mais estudo sobre o potencial desta espécie, que possui compostos fitoquímicos importantes capazes de reduzir a resistência a antimicrobianos.

As data analysis techniques progress, the focus shifts from simple tabular data to more complex data at the level of business objects. Therefore, the evaluation of such data analysis techniques is far from trivial. However, due to confidentiality, most researchers are facing problems collecting available real data to evaluate their techniques. One alternative approach is to use synthetic data instead of real data, which leads to unconvincing results. In this paper, we propose a framework to automatically operate information systems (supporting operational processes) to generate semi-real data (i.e., “operations related data” exclusive of images, sound, video, etc.). This data have the same structure as the real data and are more realistic than traditional simulated data. A plugin is implemented to realize the framework for automatic data generation.

The physical environment affects individuals emotionally, behaviorally and cognitively. Servicescapes or atmospherics studies the effect of environmental stimulation to consumers. Environmental stimuli affect consumers’ attention and cause them emotions such as pleasure and arousal; those emotional responses affect in turn consumers’ behavioral responses such us approach and avoidance tendencies towards the environment. Hence, the level of arousal-nonarousal and the pleasure-displeasure experienced by a consumer along with other intervening variables such as momentary mood and stimulus screening ability will determine his/her approach-avoidance responses towards the environmental stimuli. The paper studies atmospherics with neuromarketing and conventional marketing research methods: Specifically, using electroencephalography (EEG) and surveys, it focuses on the effect of background music on Consumer’s arousal, pleasure, attention and approach/avoidance tendencies. The results are expected to have significant academic relevance both for the servicescapes / atmospherics and the neuromarketing / consumer neuroscience research streams of literature and also managerial implications.

A 3D entangled coordination polymer {[Co4(glu)4(bpp)2]}n 1 was synthesized from a solvothermal reaction between Co(II), glutaric acid (H2glu) and 1,3-bis(4-pyridyl)propane (bpp). The crystal structure was determined by single crystal X-ray diffraction and the topological description indicates a 3D → 3D interpenetration in the pcu-coordination polymer, pillared by bpp ligands. Crystal structure presents four crystallographically distinct Co(II) metal centers conferring different coordination environments: tetrahedral, octahedral and trigonal bipyramidal. Spectroscopic (FTIR and Raman), thermal analysis and magnetic studies were employed to chemical characterization. SQUID magnetometry analysis indicated weak overall antiferromagnetic interactions between the Co(II) ions. High-Field Electron Paramagnetic Resonance (HFEPR) spectra suggest that the Co(II) ions in compound 1 are high-spin (S = 3/2). The material showed good thermal stability decomposing above 300 °C. CO2 adsorption studies carried out in the pressure range of 0 to 40 bar at room temperature revealed a favorable solid-gas interaction at low pressure. The amount of CO2 adsorbed on compound 1 continuously increased as the pressure increased and no gas saturation is observed suggesting a multilayer phenomenon of CO2 uptake. Powder X-ray diffraction analysis of compound 1 after CO2 measurements at 40 bar revealed no structural change.