A Literature Review on Predictive Monitoring of Business Processes

Oleme läbi vaadanud mitmesuguseid ennetava jälgimise meetodeid äriprotsessides. Prognoositavate seirete eesmärk on aidata ettevõtetel oma eesmärke saavutada, aidata neil valida õige ärimudel, prognoosida tulemusi ja aega ning muuta äriprotsessid riskantsemaks. Antud väitekirjaga oleme hoolikalt kogunud ja üksikasjalikult läbi vaadanud selle väitekirja teemal oleva kirjanduse. Kirjandusuuringu tulemustest ja tähelepanekutest lähtuvalt oleme hoolikalt kavandanud ennetava jälgimisraamistiku. Raamistik on juhendiks ettevõtetele ja teadlastele, teadustöötajatele, kes uurivad selles valdkonnas ja ettevõtetele, kes soovivad neid tehnikaid oma valdkonnas rakendada.The goal of predictive monitoring is to help the business achieve their goals, help them take the right business path, predict outcomes, estimate delivery time, and make business processes risk aware. In this thesis, we have carefully collected and reviewed in detail all literature which falls in this process mining category. The objective of the thesis is to design a Predictive Monitoring Framework and classify the different predictive monitoring techniques. The framework acts as a guide for researchers and businesses. Researchers who are investigating in this field and businesses who want to apply these techniques in their respective field

Probabilistic modelling and inference of human behaviour from mobile phone time series

With an estimated 4.1 billion subscribers around the world, the mobile phone offers a unique opportunity to sense and understand human behaviour from location, co-presence and communication data. While the benefit of modelling this unprecedented amount of data is widely recognised, a number of challenges impede the development of accurate behaviour models. In this thesis, we identify and address two modelling problems and show that their consideration improves the accuracy of behaviour inference. We first examine the modelling of long-range dependencies in human behaviour. Human behaviour models only take into account short-range dependencies in mobile phone time series. Using information theory, we quantify long-range dependencies in mobile phone time series for the first time, demonstrate that they exhibit periodic oscillations and introduce novel tools to analyse them. We further show that considering what the user did 24 hours earlier improves accuracy when predicting user behaviour five hours or longer in advance. The second problem that we address is the modelling of temporal variations in human behaviour. The time spent by a user on an activity varies from one day to the next. In order to recognise behaviour patterns despite temporal variations, we establish a methodological connection between human behaviour modelling and biological sequence alignment. This connection allows us to compare, cluster and model behaviour sequences and introduce novel features for behaviour recognition which improve its accuracy. The experiments presented in this thesis have been conducted on the largest publicly available mobile phone dataset labelled in an unsupervised fashion and are entirely repeatable. Furthermore, our techniques only require cellular data which can easily be recorded by today's mobile phones and could benefit a wide range of applications including life logging, health monitoring, customer profiling and large-scale surveillance

A New Time Series Similarity Measurement Method Based on Fluctuation Features

Time series similarity measurement is one of the fundamental tasks in time series data mining, and there are many studies on time series similarity measurement methods. However, the majority of them only calculate the distance between equal-length time series, and also cannot adequately reflect the fluctuation features of time series. To solve this problem, a new time series similarity measurement method based on fluctuation features is proposed in this paper. Firstly, the fluctuation features extraction method of time series is introduced. By defining and identifying fluctuation points, the fluctuation points sequence is obtained to represent the original time series for subsequent analysis. Then, a new similarity measurement (D_SM) is put forward to calculate the distance between different fluctuation points sequences. This method can calculate the distance of unequal-length time series, and it includes two main steps: similarity matching and the distance calculation based on similarity matching. Finally, the experiments are performed on some public time series using agglomerative hierarchical clustering based on D_SM. Compared to some traditional time series similarity measurements, the clustering results show that the proposed method can effectively distinguish time series with similar shapes from different classes and get a visible improvement in clustering accuracy in terms of F-Measure

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications

사물인터넷 기반 성과 측정 시스템에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 공과대학 산업·조선공학부, 2017. 8. 박진우.The ability to measure operational performance is an important factor for competing enterprises in the global market. Performance measurement helps in the evaluation of the long term effects of outputs for improving competitiveness and decision-making power. A companys competitiveness and profits are reduced by a consistent continuation of subpar performance, as this eventually leads to a failure to meet customer need. In this overall perspective, using performance measurement to understand the companys circumstances is necessary for the manufacturing system to have rapid reactive ability. Although manufacturing companies have used information systems to manage performance, there has been the difficulty of capturing real-time data to depict real situations. The recent rapid proliferation of Internet of Things (IoT) has enabled the resolution of this problem. With the maturity of IoT devices and databases technology, manufacturers are able to assess productivities and obtain real-time feedback from all production lines through IoT data. As IoT-based environment is well established, Industry 4.0 has evolved. It is the fourth stage of industrialization, and is also referred to as smart factory. Indubitably, in a smart factory environment, the complexity of information system network has increased, because manufacturing systems consist of multiple servers and client applications. Interoperability among manufacturing information systems is a rising issue for a manufacturer who developed the inter-connected systems and systematic obedience. OPC-UA (Open Platform Communication Unified Architecture) is a set of industrial standards providing a common interface for communications and represents a method to transmit any kinds of data. This thesis follows OPC-UA standard and explains how IoT data are exchanged among heterogeneous systems. Moreover, complexity of network causes IoT fault. If an IoT fault occurs, the performance measurement results cannot describe the production situation appropriately, because data-driven measurement is strongly connected with acquired IoT data. In other words, a reasonable value for Key Performance Indicators cannot be derived, if the IoT data have an error value. An IoT data anomaly detection and mitigation process is therefore required in response to the problem. To resolve enumerate backgrounds and problems, the dissertation comprised five steps: (1) Development of an smart factory performance measurement model consistent with the ISA-95 and ISO-22400 standards, which define manufacturing processes and performance indicator formulas(2) Identification of IoT applicable parts in ISO-22400 standard and selection of the Key Performance Indicators of the Net-Overall Equipment Effectiveness (OEE)(3) Configuration of the smart factory architecture and performance measurement process using Business Process Modelling, and adaptation of data exchange protocol by referencing OPC-UA(4) Implementation of an IoT fault case classification and data anomaly detection and mitigation algorithm, using k-means and statistical inference methodsand (5) Validation of the proposed system through experimental simulation. The experimental simulation results showed that the proposed system represented the timestamp data acquired by IoT and captured the entire production process. In addition, these results indicated that the proposed data anomaly detection and mitigation algorithm have a positive impact on IoT data anomaly identification, thus enabling the determination of real-time performance indicators.Net Overall Equipment Effectiveness ４０ 3.3. Suggestion of smart factory production performance model ４４ Chapter 4. Implementation of smart factory performance measurement system ４７ 4.1. Configuration of smart factory architecture for performance measurement ４７ 4.1.1. Development of network architecture ４７ 4.1.2. Designation of business logic with BPMN ５５ 4.2. Adaptation of OPC-UA ５９ Chapter 5. Development of the IoT data anomaly detection and mitigation algorithm ６６ 5.1. Classification of the data anomaly types ６６ 5.2. Designation of the data anomaly response model ６９ 5.2.1. Data anomaly detection algorithm ６９ 5.2.2. Data anomaly mitigation algorithm ７５ Chapter 6. Execution of experimental simulation study ８０ 6.1. Creation of IoT-based smart factory ８０ 6.2. Execution of factory simulation ８３ 6.2.1. Simulation of normal (Error-free) IoT data case ８３ 6.2.2. Simulation of abnormal IoT data case ８７ 6.3. Results analysis and validation of the proposed algorithm ８９ Chapter 7. Conclusion １０４ 7.1. Discussion of findings and future works １０４ 7.2. Conclusion １０６ APPENDIX １０７Chapter 1. Introduction １ 1.1. Performance measurement １ 1.2. Manufacturing information system ５ 1.3. Internet of Things and smart factory ７ Chapter 2. Overview of this dissertation １０ 2.1. Problem definition １０ 2.2. Research statement １３ 2.3. Literature reviews and outlook of the dissertation １６ Chapter 3. Development of smart factory production performance model ２３ 3.1. Introduction of international standards ２３ 3.1.1. Introduction of ISA-95 (IEC-62264) ２３ 3.1.2. Introduction of ISO-22400 ２９ 3.2. Identification of key performance indicators ３２ 3.2.1. IoT applicable parts in ISO-22400 ３２ 3.2.2. Selection of key performance indicatorDocto

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

IoT Security Evolution: Challenges and Countermeasures Review

Internet of Things (IoT) architecture, technologies, applications and security have been recently addressed by a number of researchers. Basically, IoT adds internet connectivity to a system of intelligent devices, machines, objects and/or people. Devices are allowed to automatically collect and transmit data over the Internet, which exposes them to serious attacks and threats. This paper provides an intensive review of IoT evolution with primary focusing on security issues together with the proposed countermeasures. Thus, it outlines the IoT security challenges as a future roadmap of research for new researchers in this domain

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)