Ildikó Szabó
email: ildiko.szabo2@uni-corvinus.hukatalin.ternai@uni-corvinus.hu

Katalin Ternai

Process-based analysis of digitally transforming skills

Keywords: digital transformation, business process, process ontology, skilled workforce

In Industry 4.0 a lot of jobs will be replaced by machines due to the technological revolution. Digital transformation entails new skills required to possess by people. This paper presents a solution to create data warehouse to assess future job skills based on the actual industrial business processes. The solution collects time series data from job portals and transforms them into the data warehouse to analyse skill sets. The structure of the data warehouse and the algorithm of extracting data from job vacancies have been introduced.

Introduction

The essential goal of Industry 4.0 is to push the manufacturing sector to its next transformation -in order to become more competitive, need to embrace emerging technologies, such as advanced analytics and cyber-physical system-based approaches to improve the efficiency and productivity. The key benefits of Industry 4.0 are to make business processes faster, more efficient and more customer-centric, while at the same time going beyond automation and optimization and detect new business opportunities and models. The human and social dimension are general in Industry 4.0. Tasks and demand for employees may change, however, different skills will be required [START_REF] Gorecky | Human-machine-interaction in the industry 4.0 era[END_REF].

New prophecies speak about jobs will be replaced by machines due to this technological revolution. As we learned from the prior industrial revolutions, job roles are transformed into new ones which require new or improved competences to execute their related activities.

Skill relevance of digitally transformed business processes

OECD and CEDEFOP have been investigating skills of future work since many years. OECD report published in 2016 emphasized the importance of ICT foundation skills to get better jobs and high wages by jobseekers. Inequality in possessing these skills is experienced among different age groups. Young adults are more prepared for the digital economy than the older ones. Jobs requiring more intensive ICT entail needs for other skills like "a solid level of information processing skills as well as the ability to collaborate, share information, give presentations, provide advice, work autonomously, manage, influence and solve problems" and social skills [START_REF]OECD: Skill for a Digital World[END_REF]. CEDEFOP survey also states that "at least moderate-level ICT skills also require a strong level of complementary skills, such as foundation skills (literacy, numeracy), soft skills (planning and organization) and behavioral skills (communication and teamwork). Jobs requiring advanced ICT skills depend heavily on people being able to solve problems, learn, adapt, apply new methods and technologies as well as in-depth technical knowledge". Highly risked groups for having digital skill gap are women, older-aged and lower-educated workers. Professionals in high skill intensive jobs must update their skill to avoid this gap [START_REF]Briefing note -People, machines, robots and skills[END_REF]. The research conducted by the Institute for the Future for the University of Phoenix Research Institute revealed ten new skills based on six key drivers of change: sense-making, transdisciplinary, novel and adaptive thinking, social intelligence, new media literacy, computational thinking, cognitive load management, design mindset, cross cultural competency and virtual collaboration [START_REF] Davies | Future work skills 2020[END_REF].

Organizational culture in the environment as Industry 4.0 must be a facilitator of learning and create behaviors in jobs. This requires changes in management practices to provide an appropriate organizational climate. Managerial skills can be derived from these management approaches: making organizational structure more adaptive, knowledge oriented leadership, HR practices or the cut-off of traditional investment style [START_REF] Shamim | Management approaches for Industry 4.0: A human resource management perspective[END_REF].

Approaches for analyzing skill needs

The studies conducted from macro perspective emphasizes the importance of ICT and related hard/soft, transversal skills in the future. But the organizational perspective can discover new areas like managerial practices within a learning and innovative organizational climate. Nevertheless, the comprehensive studies highlight the role of information systems to continually monitor and predicate skill needs and the commitment of educational institutions to integrate them into their training curricula and programs [2][3]. Studies, projects, frameworks, information systems built on key indicators or data tables deal with the analysis of skill needs. The New Skill for New Jobs initiative "is intended to promote an improvement in skills forecasting and matching the supply of skills to the needs of the labor market through better cooperation between the worlds of work and education." [START_REF]Policy initiatives in the field of education: Short overview of the current situation in Europe[END_REF] The New Skills Agenda for Europe determines action points to equip people with new skills, transferring information to facilitate their job seeking and to improve their life chance. It is planned to provide upskilling pathways for adults, review European Qualification Framework, strength the co-operation among education, employment and industry stakeholders, develop a new framework for strategic cooperation between stakeholder to bridge short and medium-term skill gap to support the sectoral strategy [START_REF]New Skills Agenda for Europe -Employment, Social Affairs & Inclusion -European Commission[END_REF]. More and more governmental data and other relevant data sources are available on the Internet due to the Linked Open Data, Open Data and Open Government Initiative. Skills Panorama created by the European Commission and developing by Cedefop "is a central access point for data, information and intelligence on skill needs in occupations and sectors that provides a European perspective on trends in skill supply and demand and possible skill mismatches, while also giving access to national data and sources." Research studies are provided about employment and future skills per country, per industry, per occupation [START_REF]Skills Panorama[END_REF]. Skills related statistics are available on the Eurostat portal. Employment, labour market supply and demand can be queried based on time series data along different dimensions (industry, occupation etc.). [START_REF]Eurostat -Data Explorer[END_REF] ESCO (European Skills/Competences, Qualifications and Occupations) connects these three pillars of labour market and classifies their concepts into a comprehensive, ontology structure which will be queried semantically a SPARQL interface, following the Linked Open Data initiative [START_REF]ESCO -ESCOpedia -European Commission[END_REF]. These data sources or analytical tools [START_REF]Skills Panorama[END_REF][9] applied mainly the macro perspective and not the organizational perspective, because skill or competence sets are not analysed per industry or per occupation. ESCO ensures the bridge to connect analytical results per occupation to the skills of these occupations. Time series data of these competence sets are required to detect patterns or trends in this context. Job vacancies posted on popular job portals can provide these time series data and the contextual information about industry, occupations/position, activities, region etc. as well. During this research, a data warehouse is building from these information to provide further analysis of skill sets to mix macro and organizational perspective as well. Section 2 presents the related theoretical background of this research. The structure of this data warehouse and the algorithm of extracting these data from job vacancies are introduced in Section 3. Its implementation and test run are showed in Section 4. Conclusions about future work are drawn in Section 5.

2

Theoretical background

Semantic business process modelling, Process ontology

Ontologies as well as semantic web have key role in semantic business process management (SBPM) [START_REF] Hepp | An Ontology Framework for Semantic Business Process Management[END_REF]. Truly the semantic web relies heavily on ontologies to structure data for comprehensive and transportable machine understanding. Ontologies are an integral part of semantic web in facilitating knowledge sharing and reuse. In the field of SBPM, ontologies can be utilized for knowledge representation, knowledge engineering, information modelling, database development and integration, information retrieval and extraction, knowledge management and mining, and agent-based system development. They are used to share technical and business information throughout an organization or even in extended or virtual enterprises. An ontology is used as a mechanism for expressing and sharing enterprise knowledge to support intelligent queries.

Among the wide spectrum of approaches top level, domain and task level ontologies can be distinguished.

 Top-level ontologies -are used to represent the building blocks for a particular domain.

 Domain level ontologies -are used to represent vocabulary related to certain domains.  Task level ontologies -are used to represent vocabulary related to certain tasks. A top-level ontology consists of very general terms such as "object", "property", "relation" that are common across domains. These ontologies are the first step towards knowledge representation for any domain. Terms in the domain ontology are ranked "under" the terms in the upper ontology and stand in subclass relations. Zhou and Kuntz [START_REF] Zhou | Manufacturing ontology analysis and design: towards excellent manufacturing[END_REF] have developed a top-level ontology for representing the fundamental terms of a company, i.e. product, organization, activity, actor, facility, method and value. The framework consists of generic entities for providing products and services using material flow, information flow, and cost flow. Gialelis et al. [START_REF] Gialelis | Manufacturing collaborative process integration utilizing state of the art technologies[END_REF] presented an ontological model for applications and systems that participate in collaborative processes. They have proposed an architecture that combines web services, ontologies, and workflows for efficiently carrying out the integration process under a collaborative environment. Here the integration is carried out by describing the enterprise processes by means of workflows and by using web services as a channel of communication within an enterprise. They test case was a procurement process carried out by suppliers for ordering materials, spare parts, tools, etc.. Task level ontologies are used to model the entities performing their respective operations and support interaction and interoperability among them. Generally, these ontologies are implemented in the field of multi-agent systems to model the tasks of various agent systems. Merdan et al. [START_REF] Merdan | Knowledge-based multi-agent architecture[END_REF] presented a knowledge-intensive multi-agent architecture that enabled ontology-based communication and cooperation among a set of autonomous and heterogeneous agents.

Several enterprise information models are present in literature that describe the structure and relationships of data and information elements within enterprise information systems, such as CIMOSA, MOSES, MISSION, FDM [START_REF] Merdan | Knowledge-based multi-agent architecture[END_REF][START_REF] Canavesio | Enterprise modelling of a project-oriented fractal company for SMEs networking[END_REF][START_REF] Lin | A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration[END_REF][START_REF] Fox | Enterprise modeling[END_REF]. These models have been developed for intra-enterprise integration, while some generic ontology models have been proposed for enterprise integration such as TOronto Virtual Enterprise (TOVE), GRAI, GERAM, ARIS, Enterprise ontology, PSL, Electronic business using eXtensible Markup Language (ebXML), Business Process Modelling language (BPML), etc. [START_REF] Schlenoff | An analysis and approach to using existing ontological systems for applications in manufacturing[END_REF][START_REF] Schlenoff | Knowledge engineering and ontologies for autonomous systems[END_REF].

The Enterprise Ontology was developed under the Enterprise project to provide a collection of terms and definitions relevant to business enterprises [START_REF] Goossenaerts | Ontology and enterprise modelling[END_REF]. The Enterprise Ontology was developed as a generic model oriented towards business and organizations. TOVE has been developed to support enterprise integration and communication [START_REF] Fadel | A generic enterprise resource ontology[END_REF]. TOVE provides a generic and reusable ontology for modelling enterprises.

BPML represents a process definition language intended for expressing abstract and executable processes that address all aspects of enterprise business processes [START_REF] Arkin | Business process modeling language (BPML)[END_REF]. It provides an abstract execution model for collaborative and transactional business processes based on the concept of a transactional finite state machine. ebXML is a family of XML standards sponsored by OASIS and UN/CEFAT [START_REF] Waldt | the global standard for electronic business[END_REF]. It enables enterprises to conduct business over the internet.

Ontologies, as general but formalized representation can be used for describing the concepts of a business process. According to our research, process ontologies have no precise definition in the academic literature. Some refer to it simply as a conceptual description framework of processes [START_REF] Herborn | Process ontologies facilitating interoperability in egovernment, A methodological framework[END_REF]. In this interpretation process ontologies are abstract and general. Contrary, task ontologies determine a smaller subset of the process space, the sequence of activities in a given process [START_REF] Benjamins | Assumptions of problem-solving methods and their role in knowledge engineering[END_REF].

The process ontology is used to reconcile the heterogeneous semantics of process modeling constructs, i.e. meta-model semantics existing in different process modeling languages. It indicates that process ontology should include a set of meta-concepts that are able to describe the semantics of process models.

In this paper the concept of process ontologies is used, where ontology holds the structural information of processes. The solution of establish the links between process model elements and ontology concepts has been prepared in the methodology. The attempt is to provide an extension for the standard ontology definition in the form of an annotation scheme to enable ontologies to cover all the major aspects of business process definition. The approach is identified as a semi-automatic generation of BPM defined ontology.

The objective of ontology learning is "to generate domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques" [START_REF] Haase | Ontology Learning and Reasoning -Dealing with Uncertainty and Inconsistency[END_REF]. Statistical, rule-based or hybrid ontology learning technique are distinguished based on the technique to detect correlations. The pattern-based ontology learning technique is one of them [START_REF] Zhou | Ontology learning: state of the art and open issues[END_REF]. Our solution is a pattern-based ontology learning in that process ontologies transformed from industrial business processes are used as patterns.

Labour market data warehouse

A job vacancy posted on a given job portal usually contain the following information:

Its link, position, permanent/part-time job type, region, salary, start date, company name and job descriptions. The descriptions describe the tasks, responsibilities, requirements for fulfilling the given job. These information can be structured into the following tables:

 Calendar based on the publishing date  Region: Region_ID, Region_name (from the job vacancy), Re-gion_country (from geographical database)  Industry: Industry_ID (NACE, SIC code etc. from national databases ), Industry_name (name of the given classification level), Company name (from the job vacancy)  Occupation: Occupation ID (ISCO code from ESCO based on the position name), Occupation name (from ESCO)  Job role: Job role_ID and name (from the actual process ontologies stored in databases)

 Skill: Skill_ID (unique ID), Skill name (from the actual process ontologies stored in databases and from ESCO)

Additional data sources are used to complement these information. Having created this structure different queries can be executed in this data warehouse. E.g. the distribution of skills related a given occupation per industry. The positions name, job roles and skills have to be extracted from job descriptions. But sometimes they are not so well-detailed. We need additional information in order to get these information.

Companies work along business processes whose activities must be executed by people filling the published positions. We assume that the activities mentioned in job vacancies are from these business processes.

Actual industrial business process models can reflect the organizational perspective and they can be created based on best practices. Skills to execute tasks are also determined by ESCO in these models. A database contains the process ontologies transformed from them (called as reference process ontologies).

We experienced that mainly tasks are presented in job descriptions and not competences. We use these process ontologies to identify the tasks and job roles in the job descriptions, because the relevant skills are identified based on them from the process ontology. The text mining algorithm is detailed in [START_REF] Szabó | Semantic Audit Application for Analyzing Business Processes[END_REF].

Test

By means of a concrete process from hospitality industry, a job portal called as JobSite from United Kingdom, we show the applicability of the method.

The first step is to determine the structure of a process model (task as process step, role as job role and required skill to execute this task by this role) and transform it into a process ontology.

In the use case the business process models were implemented by using BOC ADONIS modeling platform. [START_REF]Business Process Management with Adonis[END_REF] Our approach is principally transferable to other semi-formal modeling languages. There are several parameters that can be set or defined when modeling a business process in this tool, and in others as well. The shell of a business process can be easily formed with activities, decision points, parallelism or merging objects, logical gateways and events. The 'Supplying' business process model can be seen in Figure 1.

Fig. 2. The 'Supplying' process

The activities in the process:

Supplying Necessary Inputs

Process start Description Includes providing various kinds of goods and materials for the hotel business as a whole. These elements preferably influence the range and quantity of necessary goods and materials. Consumables character of the material, or the need for its continuous renewal, conditioned by the continuity of the business process. Given that certain types of goods and perishable foods subject, there is no possibility of forming a stock of goods of that kind, or the process requires special conditions for its storage. To map conceptual models to ontology models we have used meta-modeling approach. Meta-models offer intuitive way of specifying modeling languages and are suitable for discussion with non-technical users. Meta-models are particularly convenient for the definition of conceptual models. In our approach, we establish the links between model elements and ontology concepts. As ontologies provide semantics, they can describe both semantics of the modeling language constructs as well as semantics of model instances. [START_REF]Business Process Management with Adonis[END_REF] For the mapping the conceptual models to ontology models, the business process models are exported in the structure of ADONIS XML format. All objects from the business process model will be an 'instance' in the XML structure, the attributes have the tag 'attribute', while the connected objects (such as the performer, or the input/output data, which are stored in another model in the Adonis tool) have the tag 'interref'.

Select and certify suppliers

The "conceptual modelsontology models" converter maps the Adonis Business Process Modeling elements to the appropriate Ontology elements in meta-level. The model transformation aims at preserving the semantics of the business model. The general rule we follow is to express each ADONIS model element as a class in the ontology and its corresponding attributes as attributes of the class. This transformation is done by the means of XSLT script which performs the conversion. The converted OWL ontology in the structure of Protege/OWL XML format is imported into the editor of Protege 4.2.

To specify the semantics of ADONIS model elements through relations to ontology concepts, the ADONIS business model first must be represented within the ontology. Regarding the representation of the business model in the ontology, one can differentiate between a representation of ADONIS model language constructs and a representation of ADONIS model elements. ADONIS model language constructs such as "activity", as well as the control flow are created in the ontology as classes and properties. Subsequently, the ADONIS model elements can be represented through the instantiation of these classes and properties in the ontology. The linkage of the ontology and the ADONIS model element instances is accomplished by the usage of properties. These properties specify the semantics of an ADONIS model element through a relation to an ontology instance with formal semantics defined by the ontology. The final ontology can be seen in the Protégé editor in Figure 4. The second step is to download job vacancies from the job portal and run the abovementioned algorithm to extract skills. A Java program was written to perform this step. 808 job description were processed by the algorithm. All process steps were identified, but the roles were called by different name in the job description. The accuracy of this algorithm must be tested by a confusion matrix in the future.

Acknowledgment

This research was supported by the project nr. EFOP-3.

Conclusions

This paper presented a solution for collecting time series data from job portals and transforming them into a data warehouse to analyse skill sets in a deeply manner than in the case of Skills panorama, or the Eurostat databases. Some difficulties can emerge from data cleansing and identifying skill needs in job descriptions. Specific skills can be appeared in them which have to be extracted with an other algorithm. New scripts of functions are required to overcome these problems.

As future work we have to extend our algorithm with them. We can complement our solution with the results of Cedefop. OECD or the Institute for the Future. The skills mentioned in their report can be stored besides the process ontologies. It is worth to detect them in job descriptions to follow their history in the job vacancies: they are required by which occupation in which industry from when till when.

The occupational trends appeared in the Eurostat data can help to evaluate the future importance of collected skill sets, but this is the topic of an other connecting research.

Fig. 1 .

 1 Fig. 1.

Fig. 3 .

 3 Fig. 3. The process ontology in Protege 5.0

Fig. 4 .

 4 Fig. 4. Results of the extracting algorithm

  6.2-16-2017-00007, titled Aspects on the development of intelligent, sustainable and inclusive society: social, technological, innovation networks in employment and digital economy. The project has been supported by the European Union, co-financed by the European Social Fund and the budget of Hungary.


Jose Granados

Tomi Westerlund

Lirong Zheng

Zhuo Zou

IoT Platform for Real-time Multichannel ECG Monitoring and Classification with Neural Networks

Keywords: IoT, ECG, Healthcare, AI, Neural Networks

Internet of Things (IoT) platforms applied to health promise to offer solutions to the challenges in healthcare systems by providing tools for lowering costs while increasing efficiency in diagnostics and treatment. Many of the works on this topic focus on explaining the concepts and interfaces between different parts of an IoT platform, including the generation of knowledge based on smart sensors gathering bio-signals from the human body which are processed by data mining and more recently, deep neural networks hosted on cloud computing infrastructure. These techniques are designed to serve as useful intelligent companions to healthcare professionals in their practice. In this work we present details about the implementation of an IoT Platform for real-time analysis and management of a network of bio-sensors and gateways, as well as the use of a cloud deep neural network architecture for the classification of ECG data into multiple cardiovascular conditions.

Introduction

The advancement of Internet of Things (IoT) platforms has promised to solve many of the challenges that healthcare systems worldwide face today. The IoT platforms, which refer to a comprehensive system involving the interconnection of smart sensors to cloud computing services, has spawn a new paradigm in the way healthcare services can be administered. The traditional setup in which a patient visits the physician's office, where he or she has to recall the symptoms from memory is being re-imagined from a reactive stand point to a proactive one. This is possible thanks to smart sensors that gather bio-signals from the human body which are transmitted to cloud services via intelligent web-enabled gateways as shown in Fig. 1. The health signals then pass through an analysis phase consisting in applying data mining and machine learning algorithms which can diagnose conditions with human comparable accuracy [START_REF] Rajpurkar | Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks[END_REF]. This way, intelligent systems can provide continuous secure monitoring of physiological parameters in order to detect patterns that might represent an abnormality. This methodology allows to automatically suggest lifestyle changes learned from previous cases or if the Fig. 1. IoT Health Workflow condition is critical, alert the corresponding healthcare professionals to act on time before the ailment turns into a dangerous or expensive disease. Considering that most hospital visits are due to over-treatment, redundant, inappropriate, or unnecessary tests and procedures that can be diagnosed and treated at home, these intelligent systems enable the optimization of resource allocation to the more demanding cases.

IoT Platforms for healthcare still face significant obstacles in terms of performance, power consumption and functionality in order to unleash all the possibilities that they can provide. Many of the works in this topic study the implementation of specific cases for monitoring different physiological parameters. These bio signals often require distinctive transmission and processing needs which depend upon a particular solution. However, a comprehensive system that allows the monitoring of a network of sensors through embedded connected devices, as well as the management of a set of gateways from a cloud service in real-time with the hardware and software engineering details has been more or less still a work in progress. Data mining, and more recently Deep Learning has also played the fundamental role of classifying signals for diagnostic purposes with humanlike accuracy. The potential of implementing Artificial Intelligence modules to the real-time processing of bio-sensor data in the context of an IoT platform for Healthcare therefore could have beneficial implications which could ameliorate healthcare services and revolutionize the insurance industry.

Workload Balance

An important aspect to consider is the workload balance between different system components, namely the edge node, the gateway and the cloud computing modules. In order to ensure readiness and reaction time against health risks, the wearable devices must have up to date detection algorithms available or have permanent access to them on the cloud in real time. Using the cloud approach, the process is similar to constantly ask the cloud service provider for the latest diagnostics and treatments so that they can be presented to users immediately, where the cloud is the knowledge repository that has been learned so far from previous cases and is continuously expanding. Eventually and as edge node microprocessors become more powerful and energy efficient, the devices could train the neural network locally and then share the trained package with other devices in a peer to peer, decentralized fashion. In this scenario, there is less need to regularly upload personal bio-signals which can reduce privacy concerns, and instead only the learning, represented as neural networks weights, is communicated [START_REF] Liu | A Collaborative Privacy-Preserving Deep Learning System in Distributed Mobile Environment[END_REF]. The goal is then to achieve the optimal balance in terms of power consumption, performance, scalability and security between the different components of the system, while providing quality diagnostics and treatment in a constantly growing knowledge database. With sufficient data, the intelligence repository could achieve human-like detection accuracy of multiple conditions and propose the best evidence-based treatment while monitoring health improvement.

Mobile Device Gateway

The incorporation of mobile devices such as smartphones and tablets into people's the daily life can be leveraged to manage a network of smart sensors. These mobile devices are usually equipped with Bluetooth 4.1, 4.2 and 5 Bluetooth Low Energy (BLE) radios that can interface with low-power smart sensors in Body Area Networks (BAN). These sensors are generally accessed using the Generic Attribute Profile (GATT) protocol which presents the device properties as a database of services and characteristics. This is specially suitable for defining profiles which are definitions of possible applications and general behaviors that Bluetooth enabled sensors use to communicate with gateways as well as to characterize what kind of data a Bluetooth module is transmitting. Profiles allow the standardization of intercommunication between different kinds of sensors and gateways, even if the type of data is different, as long as they respect the way to organize use case behaviors. A mobile device gateway can also leverage established Web technologies such as Websockets, in order to send sensor data to cloud servers in real time. Websockets can also serve as a base protocol on which to implement device management frameworks that can be used to control gateways and end devices. In addition, the gateway can perform other tasks such as preprocessing for computation offloading from the node devices as well as serve as a firewall for protection against attackers outside and inside of the sensor network.

Multichannel ECG

The IoT framework proposed in this work is exemplified in a intelligent multichannel electrocardiography (ECG) monitoring and analysis system. ECG signals represent the heart activity and are used by cardiologists to diagnose cardiovascular and other diseases. The ECG signal has a particular relative high data rate and serves as a challenge for low-power embedded systems design. ECG also has been subject to different types of data mining and machine learning classification techniques particularly to detect arrhythmia, which once incorporated into a real-time IoT platform, can serve as a valuable companion tool for heart specialists. One of the goals of this work is to use ECG signals to diagnose not only cardiovascular diseases but also other kinds of correlated conditions such as pulmonary disorders. We believe that by using deep correlation and invisible features detection, abnormal signal patterns akin to multiple conditions can be exposed. An additional result from this methodology is the ability to infer context activities from the ECG that can be used to suggest better lifestyle habits for the user.

Related Work

The IoT for Healthcare research community has been active in offering solutions to the lack of efficiency and high cost of healthcare systems around the world. [START_REF] Abawajy | Federated Internet of Things and Cloud Computing Pervasive Patient Health Monitoring System[END_REF] describes the goal and the components of a complete IoT Platform for healthcare, including sensing, cloud service, data mining and end user's perspective. The focus of the IoT platform is to move the treatment to home care in order to save hospital costs and increase efficiency of physician resources. The authors use data clustering mechanisms for improving classification. However, they use a software emulator to generate the ECG data, not an embedded device capturing bio-signals, also most of the software engineering details are left out. A health monitoring system using a smartphone case with integrated electrodes and an Android application to display the data is presented in [START_REF] Mahmud | A Wireless Health Monitoring System Using Mobile Phone Accessories[END_REF]. However, no integration with cloud services or analytics are depicted. [START_REF] Mohammed | Internet of Things: Remote Patient Monitoring Using Web Services and Cloud Computing[END_REF] shows an Android application connected to an ECG device and to a cloud database. The details of the implementations are explained including the use of the Bluetooth Serial profile to transmit data between the device and the Android application. In addition, the authors describe the use of a SD Card to store the data locally on the smartphone. The ECG data is then uploaded to the cloud using a FTP server that saves the data on a SQL Server Filetable. This implementation nonetheless lacks real-time communication between the smartphone and the cloud server and uses MATLAB for visualization instead of user-friendly Web application. In addition, this solution does not adopt open source components, and seems to require an external Linux of Windows computer to transfer the data from the smartphone to the cloud server using FileZilla. An integrated solution in the form of an ASIC for measurement and analysis of ECG including keying for security purposes is presented in [6]. The authors perform Verilog simulations using Modelsim and Synopsys tools to verify the functionality of the design. The end-to-end system is designed using Verilog-HDL, and a test-bench is created to simulate it by modeling the input data. The resulting ASIC achieves a reduction of 62.2% in power consumption and a 16% reduction in area when compared to similar state-of-the-art processors. The work in [START_REF] Amir | Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach[END_REF] describes a smart gateway for e-health which serves as a bridge between numerous sensor protocols and to execute preprocessing of health data in order to generate health indicators before uploading to the cloud. Fog computing is introduced at the gateway network layer in order to reduce the latency of the decision making process which is performed locally instead of in the cloud. A wearable sensor node with energy harvesting and Bluetooth interface with HTML5 based smartphone app is presented in [START_REF] Wu | An Autonomous Wireless Body Area Network Implementation Towards IoT Connected Healthcare Applications[END_REF]. The battery-less device is able to work 24 hours on adequate sunlight conditions and can transmit sensor data to a Web based smartphone application designed to display signals and send emergency notifications.

The current literature makes known the general components and concepts of an IoT platform for Healthcare such as bio-sensors, cloud computing, data analysis and visualization. However, there is still room for empirical studies in building such a system. We consider that it is practically important that the details of the actual software and hardware engineering are explained, as there is a pragmatic need to build a solid infrastructure in a way such that cost, performance and scalability assessments of real world implementations can be achieved. Current research is focused on describing specific use cases like transmitting a physiological parameter variable to the cloud using definite methodologies as well as explaining the main components of an IoT platform such as the edge nodes and cloud services without digging too much into the details of the implementation. We propose to create a general IoT framework that can be mapped to well established technologies such as Bluetooth LE, CoAP, Websockets, NoSQL databases, etc with a solid reasoning behind the selection and development of technologies used in order to obtain a comprehensive, well structured solution.

Real-time IoT Platform for Healthcare

We propose an IoT Platform for ECG analysis as shown in Fig. 2 consisting of a smart ECG sensor, Web-enabled gateway based on a smartphone and cloud server for monitoring and managing devices integrated with a deep neural network for classification of signals. Emphasis is done on the implementation of real-time capabilities by using Bluetooth LE GATT notifications on sensor nodes and Websockets on the gateway and cloud server link. That way we can achieve not only near real-time data transmission but also assemble a base communication channel on which a device management and control framework can be built. Also, the use of GATT profiles is taken to define and translate sensor resources and services into other Internet protocols. This allows scalability by standardizing access from the gateway to the sensors and easy mapping of sensor profiles to specialized web transfer protocols similar to the Constrained Application Protocol (CoAP), which include RESTful interaction models between application endpoints, support for service and resources discovery, and other Web concepts such as Uniform Resource Identifiers (URIs) and Web Application Programming Interfaces (APIs). Finally, the ECG classification into different abnormal patterns is implemented on a multi layer Convolutional Neural Network realized using TensorFlow and hosted at the cloud. The supervised and unsupervised learning methods are used to classify the signals into normal, abnormal and as part of a cardiovascular disorder such as particular types of arrhythmia. Also, the platform incorporates a Web tool where expert ECG technicians can annotate ECG recordings which can serve as input to the supervised training network. The set of software stacks used in this implementation is shown in Fig. 3 Fig. 2. IoT Platform for ECG analysis

Multichannel ECG Monitoring Device

The ECG embedded device consist of ten electrodes that capture electrical signals from the human body. This data gathering node is shown in Fig. 4. The device was designed and built for this work and is branded as Horizon Medical IoT Holter. The 12-lead ECG graph can be realized with this configuration which, contrary to single lead configurations, allows to not only recognize a cardiovascular condition but also helps to pinpoint its location on the heart surface. The signals sensed by the electrodes are amplified and converted to 24-bit, 250 samples per second digital representation by a Texas Instruments ADS1298 analog front end, which is then inputted via Serial Peripheral Interface Bus (SPI) into a Nordic Semiconductor nRF52832 microcontroller unit (MCU) with integrated 2.4 GHz Bluetooth Low Energy compatible radio. The embedded software programmed on the device allows to configure the device as a BLE peripheral with one custom GATT service and nine characteristics, one for the status information and one for each of the eight signal channels. The characteristics are defined with a notification property which allows to push the uncompressed signal data into the central device (smartphone) and makes use of the higher data throughput feature of Bluetooth 5 in order to support the higher bandwidth requirements. The ECG device allows secure connection to the smartphone via Near Field Communications (NFC) using the LE Secure Connections with Out of Band (OOB) Pairing. In this method, a large temporary key (128 bits) is exchanged when the device is in close range of the smartphone which makes it difficult for an attacker to perform Man-In-The-Middle (MITM) attacks. If the OOB channel is immune to eavesdropping during the pairing process, then the BLE connection will also be immune from passive eavesdropping. Preliminary results obtained for the device energy consumption are shown in Table 1.

Mobile Device Gateway

The IoT gateway is a mobile device running Android or iOS operating system. An hybrid mobile app has been developed for visualization of the multichannel ECG signal as well as to forward the sensor data coming from the embedded device to the cloud server. In order to transmit the ECG signal data, the gateway creates a Websocket connection between itself and a cloud server. The Websocket protocol is secured with an OpenVPN encrypted connection. In addition, a device management framework based on top of the Websocket protocol is used to control the gateway and sensors from the remote server using an event-based API. This allows a remote server to scan for devices near each gateway and collect a list of available resources. In addition, the device management framework grants the possibility to ask specific sensors to change its operating modes such as to go from live streaming to local storage for power saving purposes. The mobile application user interface consisting of 8 live charts of sensor data is shown in Fig. 5. The mobile application is built using the Ionic Framework which allows to integrate a HTML5 based application together with native functionality into an hybrid app. The BLE Native plugin is used to establish the communication between the smartphone app and the peripheral device and allows to enable notifications from it. In the background, the Websocket connection between the app and the cloud server is implemented using the Socket.io client library. The services and characteristics are then translated into messages similar to the ones used by the CoAP protocol in order to leverage the use of URI based access to device resources as well as to perform service discovery. Preliminary results obtained for the gateway performance are shown in Table 2. In this paper we have presented a practical IoT platform for monitoring and classification of the 12-lead ECG. We have designed a Bluetooth 5 multichannel ECG monitoring device which connects to a cloud service via smartphone gateway using the GATT protocol and enabled notifications. The data is forwarded to a cloud service using a Websocket protocol which also serves as a base for a device management framework. The cloud service receives the data and passes it through a convolutional neural network which classifies the incoming signal into different types of arrhythmias. The same platform is also used to train the network by taking the signal incoming from the device and displaying it in a Web based annotation tool where heart specialists can select segments of the signal and label them. The system takes advantage of the protocols' real-time capabilities and allows scalability and workload balance between the different components of the system. This IoT platform allows to increase the efficiency of healthcare services by conducting home treatment instead of occupying valuable hospital resources. Future work includes optimizing energy efficiency on the device and gateway levels as well as implementing a FPGA accelerated neural network system for coprocessing on the cloud server.

Fig. 3 .

 3 Fig. 3. Software stacks used on the device, smartphone gateway and cloud server.

Fig. 4 .

 4 Fig. 4. 8-Channel Bluetooth 5 Embedded Monitoring ECG Device

Fig. 5 .

 5 Fig. 5. Smartphone app user interface showing multichannel sensor data.

4. 3

 3 Convolutional Neural Network ClassifierThe learning module responsible for classifying incoming ECG signals from the device as well as allowing training of the neural network is implemented using the TensorFlow version 1.3 library running on a GPU accelerated machine. A Socket.io server running on an Ubuntu 16.04 instance receives the signal data coming from the gateway. The server is implemented on Node.js version 8.4.0 and the Socket.io server used has version 2.0.3. The server also creates a connection to a RethinkDB version 2.3.6 database instance running on the same computer and stores the ECG sensor data as JSON documents containing arrays of 28 samples. On parallel, a TensorFlow engine is implemented which connects to the same RethinkDB database and queries the original raw ECG signal data. When the network is being trained, the ECG signal is first displayed on a Web based annotation tool which allows a heart technician to select waveform segments and label them according to different types of arrhythmias. The selected signals serve as inputs to train the network. The neural network is designed as succesive convolutional, pooling, reshape, fully connected and dropout layers which once trained, takes the live incoming signal from the device and automatically classifies the ECG patterns into possible arrhythmias. The classification outputs are saved into the same RethinkDB instance in order to be used by a Web application designed to show results to the physician. The Web application allows to forward automatic preprogrammed alerts depending on conditions detected or personalized recommendations prepared by the physician via chat messages displayed on the previously mentioned smartphone app used by the patient.

Table 1 .

 1 ECG monitoring device energy consumption

		Avg Current (mA)
	Advertising	4
	Transmitting	5.4
	Sleeping	0.8
	Est battery life (800 mAh)	4.5 days

Table 2 .

 2 Smartphone gateway performance

	Avg data rate in/out	15.35 KB/s
	CPU usage %	47%
	Est energy use	66 mAh
	Connection time	15m 51s
	Data sent	9.92 MB
	Device:	Samsung Galaxy S8+


Congcong Ye
email: yecongcong@sjtu.edu.cn

Chun Li

Guoqiang Li
email: li.g@sjtu.edu.cn

Lihong Jiang
email: jianglh@sjtu.edu.cn

Hongming Cai
email: hmcai@sjtu.edu.cn

A Behavior Analysis Method Towards Product Quality Management

Keywords: Behavior Analysis, Product Quality Management, Data Mining

Quality management is the basic activity in industrial production. Assuring the authenticity of testing datasets is extremely important for the quality of products. Many visual tools or association analysis methods are used to judge the authenticity of testing data, but it could not precisely capture behavior pattern and time consuming. In this paper, we propose a complete framework to excavate the features of testing datasets and analyze the testing behavior. This framework uses min-max normalization method to pre-process datasets and optimized k-means algorithm to label the training datasets, then SVM algorithm is applied to verify the accuracy of our framework. Using this framework, we can get the features of dataset and homologous behavior model to distinguish the quality of datasets. Some experiments are carried to measure the complete framework and we use various visual formats to show these results and to verify our method.

Introduction

Product quality testing data are the key factors of industrial production to discover inconsistencies and other anomalies. Ensuring the quality of data has been a continuing concern to industrial production. Testing the quality of electronic components is a tough and humdrum job. Testers are always not willing to do this work, and they sometimes directly write the testing records based on their own experience or others' data. The feature of recording result depends on the testers' psycho-behavior. Different people have different strategies and it is extremely difficult to distinguish real testing data from fictional data. At present, there is not much researches on quality of testing data, which tends to be regarded as real and authentic data. Some engineers only use simple data visual methods like histogram and scatter diagram to show the feature of data and judge the reliability of testing result. It is hard to distinguish the real data from fictional data because testers write testing records within the allowable range of error. Illusory testing data will decline the quality of industrial products and damage the corporate image.

To address the aforementioned challenges, a complete behavior analysis framework is proposed to distinguish the reliability of electronic components testing behavior. We use an optimized k-means clustering method to find the aberrant data and give every record a label. After that one third of the dataset are used as training data to optimize the kernel function of support vector machine(SVM) and another dataset are used to test the accuracy of our method. This optimized SVM can be used to distinguish the real testing data from illusory data.

Our main contributions are summarized as follows: l A joint probability distribution method is used to prove that the relationships of attributes are not fixed. The illusory testing data can not be distinguished by merely association analyzing. l An optimized clustering algorithm is proposed to mine the inner features of testing datasets. This cluster algorithm is the optimized k-means algorithm based on density and distance. l A complete behavior analysis framework is constructed to associate the dataset with behavior. The data model and behavior model can be changed according to different features of dataset. The rest of this paper is organized as follows. Section 2 discusses the related work in this field. Section 3 presents the detailed behavior analysis process including data pre-processing, data mining algorithm and behavior analysis. Section 4 uses various methods to show the result of our methods and analyzes the experimental result. Section 5 concludes the work in this paper and illustrates the future direction of this work.

Related Work

Data mining is the analysis of datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner [START_REF] Van Der Aalst W M P | Process mining in the large: a tutorial[C]//European Business Intelligence Summer School[END_REF]. It contains four major branches: clustering or classification, association rules and sequence analysis [START_REF] Kesavaraj | A study on classification techniques in data mining[C]//Computing, Communications and Networking Technologies (ICCCNT)[END_REF]. Especially, clustering algorithm is very useful when the number of classification is unknown. The major clustering algorithms are k-means, hierarchical clustering and self-organizing map algorithm. The initial K-means clustering algorithm aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster [START_REF] Mundada | Recognition and classification of speech and its related fluency disorders[END_REF]. The implementation of k-means is simple and the distance function can be changed based on the feature of datasets. Through K-means is the most common clustering algorithm, there are still some disadvantages about K-means like the choice of centers, the number of centers and so on. Many methods have been proposed to improve the performance of the K-means clustering algorithm. [START_REF] Nazeer | Improving the Accuracy and Efficiency of the k-means Clustering Algorithm[END_REF] combines a systematic method for finding initial centroids and an efficient way for assigning data points to clusters. As for hierarchical clustering, it yields a dendrogram representing the nested grouping of patterns and similarity levels at which groupings change and the clustering process is performed by merging the most similar patterns in the cluster set to form a bigger one [START_REF] Bouguettaya | Efficient agglomerative hierarchical clustering[END_REF]. Hierarchical clustering uses greedy algorithm, so the result of hierarchical clustering is only local optimum. Another common clustering algorithm is self-organizing map, which is a neural network algorithm to create topographically ordered spatial representations of an input data set using unsupervised learning [START_REF] Rumbell | A spiking self-organizing map combining stdp, oscillations, and continuous learning[END_REF]. All these common clustering algorithms can be chosen based on the features of datasets.

Another important branch of data mining is classification. Decision trees [START_REF] Dietterich | An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization[END_REF] is one of the most popular methods for classification [START_REF] Hawkins | Predicting outcomes of nonsmall cell lung cancer using CT image features[END_REF]. In decision tree algorithm, each internal node split the instance space into two or more parts with the objective of optimizing the performance of classifier and every path from the root node to the leaf node forms a decision rule to determine which class a new instance belongs to [START_REF] Dai | A mapreduce implementation of C4. 5 decision tree algorithm[END_REF].

In general, various clustering and classification algorithms can be used to dig out the inner features of datasets. However, there are not many researches about human behavior analysis. Data quality analysis method is not enough to find the behavior pattern which is reflected by the datasets if it only focuses on data features.

Methodology

A Framework of Mining Behavior Model from Datasets

The features of datasets are the reflection of testers' behavior. In order to analyze the relationship between the data model and behavior model, we construct a behavior analysis framework as figure 1. This framework consists of four modules: data pre-processing, training data construction module, behavior analysis module and data visualization module. In data pre-processing module, we can delete the outliers and use minmax normalization method to process the datasets and improve the quality of datasets.

In training data construction module, each dataset will be given a label through an optimized K-means algorithm and used to train the SVM classifier based on the association analysis and clustering result. At last, various visual methods are used to show the data mining results and behavior features. Other data mining algorithms and behavior models, instead of k-means and SVM, can also be applied in case the datasets are replaced.

Data Pre-processing

Data pre-processing includes data preparation, compounded by integration, cleaning, normalization, transformation of data and data reduction tasks; such as feature selection, instance selection, discretization, etc [START_REF] García | Data preprocessing in data mining[END_REF]. It will improve the quality of training data and increase reliability of data mining. First step of data pre-processing is data cleaning, which will dispose the noise and outliers. In order to ignore the effect of data dimension, the dataset will be normalized. There are many data normalization methods like min-max normalization, z-score normalization and decimal scaling normalization. The most common method is min-max normalization, which can transform every attribute into a value from zero to one. The calculation formula is defined as follow:

X * = X---min max---min (1)
After that we can use some attributes like mean value, variance, maximum, minimum and joint probability to excavate the features of data. All these features can reflect the fluctuation of every dataset and help engineers get an overview of dataset. However, only these methods are not enough for engineers to distinguish the authenticity of data and it needs more data mining methods to dig their inner features out.

Optimized Data Mining Algorithm

Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems [START_REF] Satyanarayanan | Tree Dataset Extraction Using HAC Based Algorithm[END_REF]. Clustering algorithms are usually used to process the datasets when their types of classification are not clear. In industrial production, the behaviors of testers depend on their different psychological activities. And we even don't know the number of different behavior types, which satisfies the conditions of clustering algorithms.

The most common clustering algorithm is k-means. Given a set of n points in ddimension and the number of centers k, the problem is to determine k points in n as centers, so as to minimize the distance from each data point to its nearest center. The accuracy of k-means depends on the choice of k centers, which is also the main imperfection of k-means. In order to solve this problem and improve the accuracy of k-means, we optimize the original k-means algorithm and propose a k-means algorithm based on density and distance.

According to the relevant researches, the accuracy will be superior to others when the number of centers is set as the radication of the dataset number expect we have professional knowledge and ensure the clustering results. Center points are extremely crucial, but the original k-means algorithm chooses k centers randomly, which is difficult to get the best cluster result. Then we optimize the strategy on choosing centers. Algorithm 1 represents the main framework of an optimized k-means algorithm based on density and distance. This algorithm has four parameters and the threshold values can be adjusted according to the features of datasets. This optimized method chooses the largest density point and other evenly distributed points as centers, which will avoid the situation that center points are excessive centralization. Using this framework, the noisy points or outliers will be ignored and the accuracy will be improved.

Algorithm 1: An optimized k-means algorithm based on density and distance Input: Testing dataset dn, the number of clusters k, threshold density m, threshold radius r Output: Clustering result S k 1: for i = 0 to n do 2:

calculate the distance of any two points in dataset dis(i;j) ; 3: end for 4: delete the points when their density is lower than threshold density; 5: Collect the points in high density area D; 6: find the largest density point as the first center, and put it in the center sets Z, then delete it from D; 7: find the furthest point from the first center and put it in the center sets Z, then delete it from D repeat 8: calculate the distance product of every point in D with center sets; 9: find the furthest point and put it in Z; 10: delete it from the set D 11: Until the number of clusters do not reach k; 12: put every point in D to the suitable center which is most similar with it; 13: return Clustering result S k After clustering, we will give different clusters different labels and then use one third of these records about six hundred illusory and real records as training datasets to optimize the SVM classification. Finally, other datasets are used to judge the accuracy of our optimized algorithm and SVM. Not only this SVM can evaluate the accuracy of our labels, but also it can be used as an automated tool to distinguish illusory data quickly.

Experiments

The entire experiments were performed on a machine with 2.7GHz Intel Core i5 cpu, equipped with 8G memory. The datasets come from an electronic component factory and they consist of four batches of product testing datasets. The dataset is defined as three dimensions, which are leakage current, minimum loss angle tangent and capacitance.

Association Analysis of Each Variate

Joint probability distribution [START_REF] Horn | Joint probability distribution of environmental conditions for design of offshore wind turbines[END_REF] is an effective method to analyze the relationships between variates. In testing datasets, the crucial attributes are leakage current and min mum loss angle tangent. Hence we calculate their joint probability distribution and use 3-dimension figures to show the results. Figure 2 contains four kinds of joint probability distributions on different batches testing datasets. Z-axis represents probability density and X or Y-axis are leakage current and minimum loss angle tangent. From these figures, we know the more points in front of the plane, the quality of datasets is higher. These four figures represent four types of the testing data: the multimodal state, the singlet state, the bowl state and the strip state. All these figures show the relationships between testing variates are not sure and it is not enough for us to distinguish the real datasets from others.

Data Clustering And Label Generation

According to the formed researches, only analyzing the relationships between variates are not enough to distinguish the illusory data, hence we adopt optimized k-means [START_REF] Basu | Semi-supervised clustering by seeding[END_REF] algorithm to dig out the inner features.

Data Classification and Testing Behavior Analysis

Support vector machine [START_REF] Kisi | Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution[J][END_REF] is one of the classical machine learning techniques that can still help solve big data classification problems, especially, it can help the multidomain applications in a big data environment [START_REF] Suthaharan | Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning[END_REF]. According to the clustering results, the real datasets are not linearly separable and the points will spread over some special points uniformly. The parameters of kernel function will be changed based on the training datasets and then it will find a support vector to separate the points into different clusters. The Figure 4 is the classification result on other two third of datasets, which is t for our clustering result and speculation. Using 2400 records to verify the accuracy of classification, 2115 points are same as their labels and the accuracy is about 0.88125, which means most records can get a proper label. Hence this classification can be used to distinguish illusory data from real data quickly if the features of datasets are similar to ours. From the data model, we can infer the testers' behavior. Most of the datasets are uniform distribution, only in batch 3, the points aggregate on some fixed points. This phenomenon is similar to the crowd psychology in social psychology. For example, if a tester is not willing to measure the variates of electronic components, they will see and copy others' records to decrease the risk of mistake. At first, they do not have an uniform criterion, so it will produce fluctuating results and be steady gradually. We still need more processes to confirm our conjecture.

Conclusion

In this paper, we propose a complete data mining framework to analyze the testing behavior and distinguish authenticity of testing records. This framework is generic and many datasets can use this framework to analyze their inner features and relevant behavior model. In order to increase the accuracy of clustering, we optimize the original k-means algorithm, which will make points spread uniformly. This classification can distinguish illusory datasets quickly, which will save much manpower and resource.

The proposed method still has some limitations. Firstly, the testing behaviors depend on different psychological activities, so every state of datasets are acceptable and we can only give a speculation which datasets may be illusory. Secondly the differences between real data and illusory are very tiny, and the common normalization method may decrease even eliminate these differences. In the future, we plan to implement more data mining algorithms in our framework to support other behavior analysis. And we can invite some professors to join the experiment to verify our clustering result. Meanwhile, we will develop a data mining tool to detect the accuracy of testing datasets automatically, which will improve the quality of industry production.

Fig. 1 .

 1 Fig. 1. A Framework of Mining Behavior Model from Datasets

Fig. 2 .

 2 Fig. 2. Joint probability distribution of leakage current and minimum loss angle tangent

Figure 3

 3 is the clustering results on four batches datasets and these four batches all have six hundred records. The points in batch 1, 2 and 4 are very similar and they are equidistribution, but the points in batch 3 cluster in some fixed points, which illustrates the value of some attributes in batch 3 are same. The distribution in batch 3 is much similar to the crowd psychology phenomenon in social psychology. The crowd behavior is heavily influenced by the loss of responsibility of the individual and the impression of universality of behavior, both of which increase with the size of the crowd[START_REF] Arain | NATIONAL HIGHWAYS AND MOTORWAY POLICE IN PAKISTAN: An Illuminative Study[END_REF]. Hence we guess the third batch records are the illusory data. Then we use one third of real and illusory data as training datasets to optimize the kernel function and other datasets will be used to verify the accuracy of classifier. a. batch 1. b. batch 2. c. batch 3. d. batch 4.

Fig. 3 .

 3 Fig. 3. The clustering result of optimized k-means algorithm

Fig. 4 .

 4 Fig. 4. The Classification Result of Support Vector Machine.

Acknowledgement

This research is supported by the Shanghai Institute of Precision Measurement Project under Grand No.SAST2017-128 and the National Natural Science Foundation of China under Grant No.61373030.


Larisa Bulysheva
email: lbulyshe@odu.edu

Michael Kataev
email: kataev.m@sibmail.com

Natalia Loseva

Modeling of Service Time in Public Organization Based on Business Processes

Keywords: Process-Oriented Approach, Business Process, Mathematical Model, Time of Service, Workflow Management, Service, Public Organizations, Business Decision Making, Operational Level, Tactical and Strategic

Time is essence in all processes involved while providing customer service. Very often the duration of a specific service is regulated by the law. However, it is also affected by other external and internal factors. Quantitative approaches to model the service time and creating systems that support the workflow management process are vital. This paper proposes a model of business process analysis on the operational level for various service-oriented organizations (including governmental agencies). The model estimates time of rendering services for heavily regulated public organizations. The analysis of the obtained results is discussed, potential applications are identified, and future research directions are formulated.

Introduction

The application of business processes in the description of any enterprise or institution allows to formalize and organize core business processes. It is the transparency of the description of workflows is a crucial factor that allows the institution to achieve specified improvements in its activities. The process of developing and formalizing business processes is quite laborious and complex task, which in the end allows us to achieve success in comparison with competitors. Business processes allow you to get some "portrait" of the main working routine functions that have certain parameters. These parameters are related to the organizational structure, legislative and regulatory documents, cost and resource factors. All the factors can be divided into external and internal. Each of them, influence business processes and leads to their change. So, to maintain a given level of success, you must constantly modify business processes. This change is the only what is constant in modern business, and the change is an iterative process in nature. Extensive research is done in business process modeling, business performance management, and Enterprise Information systems [START_REF] Xu | Enterprise systems: State-of-the-art and future trends[END_REF][START_REF] Kataev | Enterprise systems in Russia: 1992-2012[END_REF][START_REF] Xu | A Novel Architecture for Requirement-oriented Participation Decision in Service Workflows[END_REF][START_REF] Li | An Efficient Recommendation Method for Improving Business Process Modeling[END_REF][START_REF] Tan | A Business Process Intelligence System for Enterprise Process Performance Management[END_REF][START_REF] Repin | Business Processes. Modeling. Implementation. Management[END_REF][START_REF] Kataev | Process oriented approach to enterprise management[END_REF]. Number of effective algorithms based on Petri nets were developed to model service workflows [START_REF] Viriyasitavat | Compliance Checking for Requirement-Oriented Service Work Flow Interoperations Transactions on Industrial Informatics[END_REF][START_REF] Viriyasitavat | A New Approach for Compliance Checking in Service Workflows[END_REF][START_REF] Van Der Aalst | The application of Petri nets to workflow management[END_REF][START_REF] Zisman | Representation, specification and automation of office procedures[END_REF][START_REF] Xu | A Novel Architecture for Requirement-oriented Participation Decision in Service Workflows[END_REF].

The model of activity of the enterprises built with the help of the business process underlies the new direction of the "virtual enterprise" [START_REF] Kataev | Enterprise systems in Russia: 1992-2012[END_REF][START_REF] Zisman | Representation, specification and automation of office procedures[END_REF][START_REF] Kataev | Influence of external and internal environment on the governmental institutions decision making processes[END_REF]. This approach allows us to build different variants of the model, depending on the possible modeling purposes. The objectives of the simulation associated with the control, management, forecasting, etc. For example, the control problem requires constructing a model that would enable a "big picture" of the institution and see the quality and quantity of the production processes in a temporary mode.

In general, the review processes of the institution through business processes (each of which has the specified parameters), allows us to build for the manager the equivalent of a "business game". The rules of the "games" allow the supervisor to observe each business process separately, as well as their combined activities. The quantitative aspect of this "game" leads to an understanding of the current state and the analysis of previous states, to assess the causes of the situation, also there is the possibility of forecasting.

Combination of technological aspects such as hardware, software and accumulated data, business processes, and people defines the needs for development of automated information system of control for public enterprise. Note, that this aspect is now rare considered in the literature in relation to public institutions providing services to general public. However, business performance management and measurement via identification of key performance indicators, usage of balanced scorecards, dashboards, and Six Sigma are well known. This allows the authors to reveal the importance of our research area and emphasize the value of formal methods development for modeling complex business processes and workflow management.

In this paper, we describe a mathematical model of transient processes of the public organizations activities at the lowest level (operational level) of business processes. We will start with a context diagram of business processes involved. Then, we will define the problems and formulate a mathematical model. Finally, we will conclude with examples of problem solved and discuss directions for future research.

Mathematical Model for Service Delivery Processes

The functional model, which reflects the currently existing processes of the subject area (model "as is"), has been created. Figure 1 shows the context diagram, where the major entities and the flows of data involved are identified. The flows of data of the model processes of the subject area which reflects the boundaries of the model in width and describes most of the processes associated with the performance of services are included. Management system is formed in accordance with the existing "Rules and regulations", where the service providers are "ministries and departments", "local government", "State, municipal and commercial institutions." The process of rendering services can be described as a sequence of phases implemented by key stakeholders. Structural diagram of this process is shown in Fig. 2.

Results of Service for a Private Customer Services

Customer

Legal Entity / Company

Results of Service for a Company Service Provider

Legislative and regulatory documents

The provision of service requests or motivated refusal

Problem Definition

Currently, there are many applications of business processes for solving problems of document management and business activities. However, there are no applications for management of the system mentioned above. There are only a few scientific papers devoted to this area of research. One reason for this is the fact that a qualitative approach is dominated in management activity of government institutions and quantitative is left out of researchers' attention. Therefore, we propose to introduce the quantitative indicators, which are calculated based on business processes. Interdisciplinary integration of methods from operations research or management science including the linear programming models could be applied to formulate mathematical models for optimization of service provider operations in any business, including public.

Let's identify the main business-processes in providing services to public customers or legal entities. Typically, these activities are: "Service" or "Advice", or both. Figure .3 shows the partitioning of the service provider day associated with the execution of certain business processes per request of services. Every day is unique due to qualification of the employees, the influence of external and internal factors. In a separate time period can be performed a variety of sequence of business processes (BP), rest (To) or running errands (Tr), as shown in Figure 3 During process of solving problems of service management many situations arise which could affect the quality of services. Figure 3 shows that the control over the activities of specialists is quite difficult without introduction of automation systems. One of the elements, which is needed to be considered, is time of service providing. Calculation of this time should be separated from the breaks time and time to implement other duties such as supervisor requests.

We propose a general mathematical formulation of the problem of organization management for service provision. The schematic of the model is shown in Figure 4. This approach allows to see the process of providing services at different levels (operational, tactical and strategic) simultaneously. Where BP OLbusiness processes on operational level, BP TL -business processes on tactical level, BP SLbusiness processes on strategic level, SMTsystem of generating the managerial action, BP OL BP TL BP SL SMT { T P } -the tactical level, calculated based on temporal measurements, T(i, j, k) the operational level.

The process of customer service, during the working time W T (work=8 hours) can be represented at the operational level in the form of the following models:

p r w T T T T    0 (1)
where r T -is the total time used for completion of customer request via all business processes, 0 T -time is not associated with the business processes, and p T -is the total time of performing supervisor requests.

Analyzing Figure 3, we can notice many different sequences in business processes that could take place while processing a customer request in a public institution. Management of employees' performance as the service providers to the public is complicated and currently poorly automated. The decision to control through usage of the special devices (cards) is currently present, however, it is not yet universally implemented. How this information could be used in management activities is the topic that needs to be investigated further and presented in the research literature.

For the solution of managerial tasks, you need to have a model that adequately, at a quantitative level describes the daily activities of the organizations. Working day description on the average values of

Model Description

Let's describe each element of the time model at the operational level of business processes in more details:

                N i i pi M j j i j i w T T T T 1 1 , 0 , (2)
where Nis a number of employees (i = 1, N) and Ma number of services (j = 1, M), j i T , -execution time of the business process associated with the service j by employee i,

j i T , 0
-time between the processes, which are not directly associated with the implementation of business processes, pi T -time that the i-th employee spends on executing other duties.

The execution time of business processes can be written as:

) ( , , , , 1 1 ,
, , , , s k d j i K k k D d d f k d j i j ri t t T         (3)
where K -total number of clients and D -number of days of service,

f k d j i t , , , , , s k d j i t , , , ,
time completion of f (finish) and the start (s) of the process s of j-th service by the i-th expert in day number d (d = 1, D).

The time between the business process services k and (k+1) customer is written like this: ) (

k d j i K k k D d d f k d j i j i t t T           (4) , , , , 1 1 1 , 1 , , , , 0 s
where K -total number of clients and D -number of days of service provision for adoption of the report, The execution time of other duties of each employee i during the d-th day is calculated as follows:

    D d d d pi pi t T 1 , (5)
When calculating the total time of providing services, the next constrains must be considered:

1)   s k d j i f k d j i t t , , , , , , , ,  15 minutes (legally defined rule) (6)
2) total working time that could be spent on providing services to population is specified as: Trab = N *D * 480 [START_REF] Xu | A Novel Architecture for Requirement-oriented Participation Decision in Service Workflows[END_REF] 3) the waiting time in queue for customers must not exceed legally defined limits in minutes:

  ) /( , , 0 , , KD t t j i j i r  30 or (8)   ) /( , 0 KD t t K j i pi   30 (9)

Solution of Managerial Problems Using Model

There are various situations that arise from influence of external and/or internal factors during the actual organization activities [START_REF] Tan | A Business Process Intelligence System for Enterprise Process Performance Management[END_REF]. The reaction to these situations by the management team is to bring the project (business processes) into a desirable state. Managerial decisions presented as the following steps: (Plan  Business process  Measurement  Analysis Solution) [START_REF] Tan | A Business Process Intelligence System for Enterprise Process Performance Management[END_REF] are cyclical in nature. The purpose of which is to detect deviations of the plan parameters (in our case, normative values) from the real values at this time. In the event of deviations from the plan there is a need to take certain administrative actions to return the situation to the plan or norm.

To make an accurate decision, management needs to know the problem which cause the situation. Empirically, it is possible to evaluate a set of circumstances that are related to the problem, and then to find the options that at the legislative level allow you to build managerial decisions. Thus, in the presence of some deviations from the planned values, the management team appears able to know the set of management decisions for direct decision making. This intellectual component of the decision-making process should contribute to the objectivity and efficiency of decision.

It is well known that the working activities of any organization linked to the business processes that are designed to provide services to general population or legal entities. Each business process is characterized by a set of input and output parameters, service providers, and managerial decisions that are based on legal and other regulatory requirements. Every day for each employee the basic elements of the expected work activities are well known, and therefore, it becomes possible to assess the main temporal elements of their activities during the day and during each work time period. The model that is presented above is based on this assumption.

Note, that it is also possible to build a model, given the fact that the working activities of each employee with various business processes are essentially sequential steps. The sequence of these steps might be recorded. For example, if we know the beginning and end of each process or if we know even just beginning time of each business process, then it gives us quantitative material for analysis. The results of the analysis offer an opportunity, despite various changes within the process, to identify the parameters to help in decision making process. Please note, that the classical ways of decision making, if we consider such approaches as scheduling, calendar planning and others [9 -11], cannot be applied in this case because the business processes served by the staff coming in a random order with a random duration (see Figure 3).

Considering that each public institution has its own specific personnel features, such as staff properties, age, qualification, culture, etc., which might be reflected in the style and types of managerial decision making, the use of the proposed model will allow to unify the decision making process, reduce the time and cost of rendering services, improve overall customer satisfaction, and to help in making higher quality and timely business decisions.

Potential Applications and Future Research

A specific feature of the temporary aspect of the organization of civil servants' activity is its intermittent nature, formed by the random arrival of different groups of clients, as well as by the large number of various orders coming from the management of different levels. As a result, employees need to constantly adjust their plans and change time intervals in their activities:

1) the implementation of all business processes associated with customers in a given time limit;

2) performance (sometimes simultaneously with the main activity) of several orders with the same deadline and the same importance. These and other conditions for the temporary organization of civil servant activity are determined by the external and internal circumstances. The external frame of the temporal behavior is formed under the influence of external normatively given rhythms (in the activity of government bodies and civil servants) and the terms (execution of instructions). The internal frame of the temporary behavior of employees is related to solving the tasks of executing business processes for each of the clients according to the normatively defined timeframe.

When solving internal route tasks, important parts are cognitive, performing, communicative and emotional components for each employee (the components of the quality of the service). The timely completion of the assigned tasks of the state organization on rendering services to clients, in the presence of external and internal factors of the temporarily behavior, forces to involve new technologies. These technologies should make it possible to minimize the subjectivity in decision-making by the manager in the complex processes of daily routines allowing access to automated technologies.

Such technology may be the approach proposed in this paper, when the concept of a business process is introduced into the basis of the activity of a governmental organization. The system of business processes allows you to describe all the processes of labor when solving the main tasks of the organization, to evaluate objectively of the activities of the process executors and to perform forecasts. Our paper serves as the basis for the development of a formal approach that is considering uncertainties and varieties in the activities of the governmental organization presented above.

Future research will be focused on continuation of model development to make it even more adequate to the variety of business applications. It might be associated with considering additional parameters, such as qualifications of each service provider, the temporary features of the arrival of clients depending on various conditions, and other. As a result of modeling, we plan to provide recommendations for business process improvements, and to assist in developing of standardized protocols of employee actions with the goal to automate processing the main or routine operations.

Fig. 1 .

 1 Fig. 1. Model of service execution.

Fig. 2 .

 2 Fig. 2. Service rendering block diagram.

Fig. 4 .

 4 Fig. 4. Model of organization management for service provision.

  -known norm and enables the assessment activities at the tactical level (from weeks to months).

  of f (finish) and the start (s) of the process s of j-th service by the i-th employee in day number d (d = 1, D).

  .

	BP1	То	BP1	То	BP1	Service Provider 1
	BP2	То	BP1	То	Тr	Service Provider 2
	BP2	То	BP2	То	Тr	Service Provider 3
	Time
		Fig 3. The sequence of business processes execution in a workday.

Acknowledgments. The authors would like to thank the IFIP Confenis 2017, General Chairs: Dr. Zhou Zou and Dr. Li Rong Zheng, and IFIP WG 8.9 members for making this conference a great success.