An Intensive Spectrum for Intention Mining Analysis

There is huge volume of data in the social networks. This data can be retrieved and integrated to extract useful meaning and come out with the insights which is called as intentions. This can be used in different fields like business, recommender systems, education, Scientific research, games, etc. Also, there are various intention mining techniques which can be applied to several fields as information retrieval, business, etc. There is no specific definition of intention mining and also there is very less existing literature present. Accordingly, there is need to conduct systematic literature review of the very recent research area. Understanding intention mining, purpose of intention mining, categories and techniques of intention mining is the need. The paper endorses a spectrum for intention mining so that further literature review of intention mining can be completed. We validate our work through dimensions, categories and techniques for intention mining

Supervised Intentional Process Models Discovery using Hidden Markov Models

Best Paper AwardInternational audienceSince several decades, discovering process models is a subject of interest in the Information System (IS) community. Approaches have been proposed to recover process models, based on the recorded sequential tasks (traces) done by IS' actors. However, these approaches only focused on activities and the process models identified are, in consequence, activity-oriented. Intentional process models focuses on intentions rather than activities, in order to offer a better guidance through the processes, based on the reasoning behind the activities. Unfortunately, the existing process-mining approaches do not take into account the hidden aspect of intentions behind the recorded users' activities. We think that we can discover the intentional process models underlying user activities by using Intention mining techniques. The aim of this paper is to propose the use of probabilistic models to evaluate the most likely intentions behind traces of activities, namely Hidden Markov Models (HMMs). This paper focuses on a supervised approach that allows discovering the intentions behind the users' activities traces and to compare them to the prescribed intentional process model

Can we sense shift in consumer behaviour in Portuguese retail companies due to the pandemic?

The 2019 coronavirus pandemic (COVID-19) has effects in the most diverse fields of our society, from mental health and lifestyle to commerce and education. A huge adaptation by the population and restructuring of habits was necessary to make progress in this new reality, leading several companies to reinvent the way they conducted their businesses and a complete metamorphosis of their business plans. As such, there was an interest in conducting this research to understand how consumer behaviour in Portuguese retail companies was affected by the lockdown in the country, aiming to identify the change in the purchase intention of consumers living in Portugal and what motivated this same change, allowing extracting information to help organizations in the decision making. Thus, 15,000 comments were collected from the social network Facebook referring to the pre-lockdown, lockdown, and post-lockdown period in Portugal. Then, data mining techniques and processes were used to clean the set of collected data and extract knowledge. Furthermore, an Intention Mining analysis was carried out to assess the collected comments and draw conclusions. Finally, the results of this study indicate a negative evolution in the purchase intention of consumers, verifying that the relationship with the company deteriorated and problems in the supply chain increased, indicating that it is necessary to redirect strategies to improve the service of customer support and distribution channels to meet customer satisfaction and may apply to other countries in similar contexts.A pandemia do coronavírus 2019 (COVID-19) tem efeitos nos mais diversos campos da sociedade, desde a saúde mental e estilo de vida, ao comércio e educação. Foi necessária uma enorme adaptação da população e reestruturação de hábitos para conseguir avançar nesta nova realidade, levando várias empresas a reinventar a forma como conduziam os seus negócios e a uma completa metamorfose dos respetivos planos de negócio. Como tal, surgiu o interesse em realizar esta investigação para compreender como o comportamento do consumidor nas empresas de retalho portuguesas foi afetado pelo confinamento no país, tendo como objetivo identificar a mudança na intenção de compra dos consumidores a viver em Portugal e o que motivou essa mesma mudança, permitindo extrair informações que permitam auxiliar na tomada de decisão das organizações. Assim, recolheram-se 15,000 comentários da rede social Facebook referentes ao período pré-confinamento, confinamento e pós-confinamento em Portugal. Em seguida, foram utilizados técnicas e processos de mineração de dados para limpeza do conjunto de dados recolhidos e extração de conhecimento. Ainda, realizou-se uma análise de mineração de intenções para avaliar os comentários recolhidos e extrair conclusões. Por fim, os resultados deste estudo indicam uma evolução negativa na intenção de compra dos consumidores, verificando-se que a relação com a empresa deteriorou-se e problemas ao nível da supply chain aumentaram, indicando ser necessário redirecionar as estratégias para melhorar o serviço de apoio ao cliente e os canais de distribuição para ir ao encontro da satisfação dos clientes, podendo ser aplicável a outros países em contextos semelhantes

Tendências do BPM

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de InformaçãoAtualmente, as organizações encontram-se inseridas em ambientes de mercado cada vez mais competitivos, deparando-se com várias dificuldades, em que face a estas, necessitam de encontrar soluções. Por essa razão, viram o BPM como uma solução para melhorar o seu negócio. Um dos objetivos do BPM é ter a capacidade de identificar, monitorar e otimizar processos de negócio cujo resultado final é um conjunto de atividades realizadas. Com base nesta monitorização e otimização, as organizações tornam-se capazes de identificar possíveis lacunas nos seus processos e com isto melhorá-los. Com isto, verificou-se a falta de informação existente cientificamente em relação à identificação de novas tendências para o BPM. Neste sentido, com este trabalho propomos realizar uma investigação seguindo a metodologia de pesquisa em Design Science Research, em que iniciamos uma pesquisa de levantamento de tendência seguindo a abordagem proposta por Webster e Watson (2002), com base em duas conferências internacionais em BPM de ranking elevado, em que se identificou os tópicos mais abordados como também problemas e soluções desde 2013 até 2015. Posteriormente, com informação recolhida ao longo de três anos, através da criação de um framework identificamos algumas tendências para o BPM, de forma a melhorá-lo. Para garantir a credibilidade dos resultados, através da criação de um inquérito por questionário realizou-se a avaliação dos resultados obtidos.Nowadays, the market gets more and more competitive, thus companies need to learn how to manage and find the right solutions for their business when facing challenges. For that reason, they saw BPM as a great tool to expand their business. One of the features of BPM is the capacity to identify, monetize and optimize processes within the business which ultimately allow for an aggregation of performed activities. Thanks to these features, the business have been capable of identifying possible gaps in their processes and how to improve them. With this, it was verified the lack of scientific information regarding the identification of new trends for BPM. Therefore, with this work we propose to conduct an investigation that follows the searching methodology in Design Science Research, where we initiate a search of lifting trends as proposed by Webster and Watson (2002). This is based on two international conferences on BPM, in which it identified the most discussed topics and also the problems and solutions since 2013 until 2015. After this investigation, with collected information over 3 years, through the creation of framework we identify some BPM trends. To approve this results, we created a survey that was held an evaluation of the final results

Unsupervised discovery of intentional process models from event logs

International audienceResearch on guidance and method engineering has highlighted that many method engineering issues, such as lack of flexibility or adaptation, are solved more effectively when intentions are explicitly specified. However, software engineering process models are most often described in terms of sequences of activities. This paper presents a novel approach, so-called Map Miner Method (MMM), designed to automate the construction of intentional process models from process logs. To do so, MMM uses Hidden Markov Models to model users' activities logs in terms of users' strategies. MMM also infers users' intentions and constructs fine-grained and coarse-grained intentional process models with respect to the Map metamodel syntax (i.e., metamodel that specifies intentions and strategies of process actors). These models are obtained by optimizing a new precision-fitness metric. The result is a software engineering method process specification aligned with state of the art of method engineering approaches. As a case study, the MMM is used to mine the intentional process associated to the Eclipse platform usage. Observations show that the obtained intentional process model offers a new understanding of software processes, and could readily be used for recommender systems

Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges

Intention mining is a promising research area of data mining that aims to determine end-users’ intentions from their past activities stored in the logs, which note users’ interaction with the system. Search engines are a major source to infer users’ past searching activities to predict their intention, facilitating the vendors and manufacturers to present their products to the user in a promising manner. This area has been consistently getting pertinence with an increasing trend for online purchasing. Noticeable research work has been accomplished in this area for the last two decades. There is no such systematic literature review available that provides a comprehensive review in intension mining domain to the best of our knowledge. This article presents a systematic literature review based on 109 high-quality research papers selected after rigorous screening. The analysis reveals that there exist eight prominent categories of intention. Furthermore, a taxonomy of the approaches and techniques used for intention mining have been discussed in this article. Similarly, six important types of data sets used for this purpose have also been discussed in this work. Lastly, future challenges and research gaps have also been presented for the researchers working in this domain

Ekstraksi dan Konversi Event Logs Dari Basis Data Sistem Rumah Sakit dan Open Source ERP Dengan Antarmuka Bahasa Alami Yang Terstruktur Untuk Kebutuhan Process Mining

Process mining merupakan sebuah cara untuk mengetahui ataupun memperbaiki alur proses sistem yang berjalan di sebuah organisasi. Process mining dapat digunakan dalam berbagai macam bidang serta dilakukan oleh siapapun yang memiliki keahlian dalam sebuah bidang. Untuk dapat melakukan process mining dibutuhkan sumberdata yang disebut dengan catatan kejadian (event log). Untuk mengambil sumber informasi yang tersimpan dalam basis data diperlukan bahasa komputer yang disebut SQL (Strucured Query Language). Sering kali seseorang yang ahli dalam bidang tertentu (Kesehatan, Sipil, Bisnis, Perbankan, dll) tidak memiliki pengetahuan lebih tentang bahasa komputer. Sehingga menyulitkan para ahli bidang tersebut untuk mengambil informasi yang mereka butuhkan dari sebuah basis data. Dengan kata lain untuk mengambil event log yang dibutuhkan dalam process mining harus melibatkan Ahli IT. Melibatkan Ahli IT pastinya membutuhkan waktu dan biaya yang tidak sedikit, belum lagi jika Ahli IT tersebut tidak sepenuhnya memahami process mining dan event log. Oleh karena itu untuk meringankan permasalahan tersebut diperlukan sebuah media perantara antara ahli bidang dengan basis data agar memudahkan pengambilan informasi (dalam hal ini Event Log). Salah satu cara mengambil informasi dari basis data tanpa menggunakan bahasa SQL adalah dengan NLIDB (Natural Language Interface to A Database), yaitu dengan memanfaatkan bahasa alami yang sering digunakan manusia untuk komunikasi sebagai masukan dengan keluaran hasil informasi yang dibutuhkan sesuai keinginan. Dari hasil pengujian pada penelitian ini telah berhasil membuat sebuah alat ektraksi event log dengan NLIDB dengan nilai akurasi sebesar 84%. Dengan begitu alat yang dihasilkan pada penelitian dapat digunakan untuk keperluan ekstraksi event log dalam bidang proses mining. Dengan menggunakan konsep pemetaan informasi semi otomatis seorang analis bisnis dapat melihat semua event log secara langsung yang berjalan dalam sebuah sistem dengan menuliskan perintah bahasa alami kemudian akan ditampilkan dalam bentuk tabel. ========================================================================================================= Process mining is a way of knowing or repair process flow system running on an organization. Process mining can be used in a variety of fields as well as conducted by anyone with expertise in a field. To be able to do the process mining required a data source called the note events (event log). To retrieve the information stored in the databases needed computer language called SQL (Strucured Query Language). Often times someone who are experts in specific areas (health, Civic, business, banking, etc.) have no more knowledge of the language of the computer. So difficult experts the field to retrieve the information they need from a database. In other words, to take the required event log in process mining should involve the IT Experts. Involving Experts IT certainly takes time and cost is not a little, not to mention if IT Experts do not fully understand the process mining and the event log. Therefore, to alleviate these problems required a medium between the experts of the field with the database in order to facilitate the retrieval of information (in this case the Event Log). One way to retrieve information from the database without using the SQL language is by NLIDB (Natural Language Interface to A Database), that is, by making use of natural language that is often used for human communication as entered by the output of the results information needed as you wish. From the results of testing on this research have successfully created a tool event log extraction with NLIDB with a value of 84% accuracy. So the resulting tool in research can be used for the purposes of extraction of the event log in the field of process mining. By using the concept of mapping information semiautomatic business analyst can see all event log directly that runs in a system with write natural language commands will then be shown in the form of a table

Potenziale von Process Mining zur Unterstützung technologiegestützten Lernens: Analyse von Computernutzung und Lernpfaden in den Raumwissenschaften

In this work the method ‚Process Mining‘ which automatically builds process models was applied to technology-enhanced learning processes. A framework was constructed to define applicable didactical support measured for students who are motivated and skilled to different degrees. With this framework as a basis, a preliminary testing study was conducted using Process Mining. Additionally the student characteristics were thoroughly analyzed. The analysis focused primarily on the students’ preconditions for learning with technology. The main results are that Process Mining can support instructors who are in charge of improving technology-enhanced learning processes. Another result was the definition of a student typology which suppliess a meaningful description of technology-enhanced learning preconditions in geosciences. The results are preliminary due to the small samples and need to be validated in additional, more representative studies.Diese Arbeit handelt von der Übertragung des Process Mining zur automatischen Modell-Generierung auf E-Learning-Prozesse. Hierfür wurde ein Framework für geeignete didaktische Unterstützung von unterschiedlich motivierten und vorgebildeten Studierenden entwickelt, welches in einer Studie untersucht und mit Process Mining unterstützt wurde. Die Eigenschaften der Studierenden wurden zudem umfassend untersucht. Der Schwerpunkt der Untersuchung waren die Lernvoraussetzungen für das Lernen mit Technologie. Die vorläufigen Hauptergebnisse sind, dass Process Mining den Erkenntnisprozess von Lehrenden unterstützen kann, um geeignete Maßnahmen zur Verbesserung des Lernprozesses abzuleiten. Weiterhin konnten Studierendencluster identifiziert werden, die die Lernvoraussetzungen für das Lernen mit Technologie in den raumbezogenen Wissenschaften wie Geologie und Geographie aufzeigen. Die Ergebnisse sind als vorläufig zu verstehen und bedürfen der Überprüfung in zusätzlichen, repräsentativeren Studien

Process Mining Versus Intention Mining

Abstract. Process mining aims to discover, enhance or check the conformance of activity-oriented process models from event logs. A new field of research, called intention mining, recently emerged. This field has the same objectives as process mining but specifically addresses intentional process models (processes focused on the reasoning behind the activities). This paper aims to highlight the differences between these two fields of research and illustrates the use of mining techniques on a dataset of event logs, to discover an activity process model as well as an intentional process model