Conceptual Model for Routine Measurements Processing and Analyses in Adaptive Intelligent Information Systems Alexander Vodyaho, Nataly Zhukova1, Maxim Lapaev1 Saint-Petersburg State Electrotechnical University, Information Systems dept, Russia 1 St. Petersburg National Research University of Information Technologies, Mechanics and Optics, Russia nazhukova@mail.ru Abstract. A significant problem of modern society is preservation and increase of resources of human brains. A person must analyze huge amounts of data, in condition of existence of multiple obvious and hidden relations in incoming data and external influence factors in order to make correct decision. Nowadays, by the most part, data to be analyzed are results of measurements of technical and natural objects parame- ters. Modern artificial intelligence based information systems are not able to process efficiently enough such type of data. It requires develop a new class of adaptive and intelligent information systems that possess fundamentally new capabilities and pro- vide new features. In the article general requirements to such systems are formulated, a conceptual model of their operation is suggested. Keywords: measurements processing and analyses, conceptual modeling, adap- tive intelligent information systems 1 Introduction Nowadays significant attention of government is paid to the problem of preservation and increase of resources of human brains in conditions of the increasing environmen- tal and information pressures and high responsibility for made decisions [1]. Separate research oriented projects are already in progress in both European countries and in Russia. Main well known difficulties of data and information analyses are huge amounts of data, which exceed possibilities of human brains for its perception and existence of multiple obvious and hidden relations in incoming data and external fac- tors that have influence on solutions of the applied problems. A relatively new challenge of data processing is lack of knowledge about problems to be solved. Often experts in subject domains are not able to identify all points that have to be checked and analyzed in the data and information streams. An essential part of analyzed data are measurements of technical and natural objects parameters. As shown in the report of IDC [2] volume of digital universe will grow by 2020 from 4.4 trillion gigabytes to 44 trillion. Only 22% of information is now considered as a candidate for analysis. These estimations proof that it is urgent to create and deploy new generation of information systems (IS) that are capable to deal with unlimited and poorly managed streams of incoming measurements. The perspec- tive approach for solving this problem is upcoming transition to adaptive and intelli- gent solutions. It requires development of new class of IS that possess fundamentally new capabilities and provide new features. In the section 2 general requirements and capabilities of such IS are discussed, the main ideas are formulated. In sections 3, 4 and 5 the proposed conceptual model for measurements processing is presented. In the section 6 the projections of the models are defined. 2 Adaptive Intelligent Information Systems requirements and expected capabilities A new class of adaptive intelligent IS (AIIS) is expected to provide support for end users that have to deal with measurement processing and analyses tasks in the con- texts of solving applied problems. AIIS Tasks. To justify expectations an AIIS has to solve following tasks: i) reduce amount of data due to its transformation to information or knowledge; ii) build linked data and information space (ISp) and permanently support its actual state; iii) enrich data, information and knowledge using all available sources; iv) provide machine based applied problems solutions regarding measurements processing [3]. AIIS Properties. An AIIS must possess following properties: i) accumulating – to be able to support continuous systematic actualization of data, information and knowledge about the target and related subject domains; extend ISp by gathering all objective and subjective data, information and knowledge which can be correlated to the problems to be solved; ii) resource saving - to be directed to saving human brain resources, spend to understanding, analysis and estimation huge amounts of data, information and knowledge gathered by IS; iii) accessible – technologies must have low cost of ownership; iv) have theoretical background i. e. use classic mathematical theories, machine learning methods, methods of intelligent analysis and pattern recognition. AIIS Distinguishing Features. AIIS uses a new approach to data processing and analysis, based on a new way of application of intelligent technologies. AIIS is to be: i) intelligent – knowledge is used in all steps and phases of problems solving; ii) automatic - IT specialists are not necessary for solving data processing and analysis problems; iii) dynamic – data processing and analysis is realized in run time or real time mode, processing procedures can be adapted in accordance with changing context; iv) able to process historical data – to be able to mine useful information and knowledge from historical data taking into account historical context. The IS with the described properties define a new class of IS, which can fit pro- cesses of measurements analyses according to the observed contexts. Intelligence of the systems allows solve applied end users tasks meaningfully using knowledge. The modern tendencies of IS design, development and support are based on the ideas of wide usage of models –architectural, information, technological and others. The ideas have taken the form of a model driven (MDE) approach [4]. The models are in fact partial derivatives (projections / views) of the general conceptual model. All models are linked. The conceptual model reflects the general ideas of the MDE ap- proach that is considered from the point of view of measurements processing domain. All other models are results of detailing the conceptual model’s varied aspects. 3 Conceptual model principals and foundation Development of the conceptual model has 3 goals: i) the conceptual model deter- mines the scope of AIIS and can be conceded as a description of the AIIS class; ii) the conceptual model is to be used as a meta model for generating model of concrete AIIS, which can be used as a part of their architectural description; iii) the conceptual model is to be used as a knowledge base for storing AIIS community knowledge about models. The proposed conceptual modal is based on a number of general ideas: 1. Real world objects are too complex for modeling, but their numerous views have simple models. 2. Real world processes are poorly predictable, too complex to be formalized, but well decomposable. 3. Feasible way to investigate the real world lies through dealing with measurements – gathering, storing, processing, analyzing. 4. Capability of consuming measurements is reachable if based on measurements progressive transformations. Model principals. The following principles are to be used for model design. 1. The main value is knowledge; it is vital to operate with knowledge in each case. 2. Any data can be meaningful; thus, all data is supposed to be carefully processed. 3. Data that is out of date is harmful; actual state of data must be supported. 4. The resources of human minds have limits; data processing and analyzes must be organized in order to provide understanding of measurements streams. 5. Both models and processes must have internal connections and external links. 6. Models and processes must be understandable by humans and machines; they must have descriptions in terms of applied subject domains and must support standards. 7. Models and processes must be easily configured; configuration must be supported by different external tools, including GUI tools. 8. Models and processes must be hierarchical and consist of multiple simple elements. 9. Models and processes must be totally oriented on support of data and information transformations. 10. Models and processes must be adaptable at the level of structure and the level of contents. 11. Models and methods used for measurements processing must be highly flexible; they have to fit exactly spontaneously changing contexts without any delay. 12. Models and processes must correspond to the desires of consumers and available resources. 13. Models and processes must be open to integrate any external data, information, knowledge and processes. Model foundation. Foundation for the model implementation form three technological stacks: transformation technologies, semantic web stack and IT technologies for IS design and support. 1. Transformations technologies. Transformation technologies relate to the domain of data, in particular, measurements processing and analyses. They allow consider pro- cesses of dealing with measurements as a sequence of transformations. Transfor- mations are defined for JDL models adapted for measurements processing (MJDL) [5]. MJDL are hierarchical functional modals that define “what is supposed to be done with measurements”. Measurements processing and analyses is considered at the level of initial measurements, objects and situations. The input and the output of the levels are defined in accordance with information models for initial measurements and results of their processing presentation. The transformations for MJDL are de- fined in the process JDL (PJDL) model. The transformations can refer to one level of MJDL, neighboring levels or to set of models. All transformations are focused on using knowledge [6]. Semantic web technologies. Semantic technologies must be considered as a world level agreement that is supported by multiple standards [7]. Their overall goal is to build giant global graph [8] interpretable and understandable by both humans and machines. Machines are responsible for solving end users problems. Experts define business politics for solving problems and provide required knowledge. Application of transformation technologies allow provide well-founded support for solving com- mon and new tasks of measurements processing using semantic technologies. One of the perspective means for dealing with measurements is described in [9]. IT technologies for system design and support. AIIS are designed, implemented and supported using agile technologies provided by IT [10]. For AIIS an adaptive approach for systems architecture design within the agile con- cept has been developed [11]. The approach is based on the following principles: 1. Reuse of knowledge is preferable in comparison with code reuse. 2. Use of ready technologies (“technologies from the shelf”) and ready platforms. 3. The benefits in the cost of development of a set of the systems that belong to one class are more important, than the benefits realized from development of one system. 4. Ontologies are used for describing classes of architectural solutions, separate solu- tions and also as means of standardization and increasing flexibility of the proposed solutions. 4 Conceptual model struсture The conceptual model (Fig. 1) gives the general view on how measurements can be consumed by humans and machines. The list of the principle entities of the model, their types and references to the groups are given in the table 1, in the table 2 types of relations between entities are briefly described. Three groups are defined according to the entities material features: i) real entities (RE) - group of real world entities (ob- jects of the real world, AIIS, tools and means); ii) model entities (ME) - group of Environment Environment Measurements Measurements Processing Processing and and analysis analysis AIIS AIIS arcitecture arcitecture General General Archtectural Archtectural solutions solutions and and means means of of its its IMplementation IMplementation GM E MSD MPA GAM AIIS GOM AIIS creates Tools Tools creates Is a part of Is a part of build based on HAF AIIS ME defines GM MPA HAD AIIS Researcher describes Is build on the base of build based on build based on fills E M MPA AD AIIS Is build by means of AF AIIS designs used for processing and analysis used for data describes designs operates in is build according to transmission Architect used for data processes TS TMIS transfer M agrees with AIIS transmission Real Real Objects Objects configures describes describes describes fills describe fills M AIIS M TS M TMIS MM defines End user is a part of is a part of Is used for filling up build based on build based on build based on SM AIIS DM AIIS used by AIIS for filling up IS AIIS AIIS Models Models used by AIIS for filling up IS fills GM TS GM TMIS GM M used by AIIS for filling up IS Sponsor General General Models Models Environment Environment AIIS AIIS -- Fig 1. The structure of the proposed model for measurements processing and analyses models (general models, AIIS models); iii) logical entities (LE) - logical domains (environment, domain of measurements processing and analyses). Table 1. List of the principle entities of the conceptual model Entity id Entity name Entity Relation type to group TS target system TSE RE M TS model of the target system ME GM TS general model of the target system TMIS TMI system TMIE RE M TMIS model of the TMI system ME GM TMIS general model of the TMI system M measurements IPME RE MM model of measurements ME GM M general model of the measurements AIIS adaptive intelligent information system SAE RE M AIIS model of AIIS ME SM AIIS static model of AIIS DM AIIS dynamic model of AIIS AD AIIS architectural description of AIIS HAD AIIS hierarchy of the architectural descriptions of AIIS GAM AIIS general architectural models of AIIS GOM AIIS general ontological models of AIIS AF AIIS architectural framework of AIIS AFE RE HAF AIIS hierarchy of architectural frameworks of AIIS ME E environment TSEE LE ME model of the environment ME GM E general model of the environment M MPA model for measurements processing and analyses of TS MPAE ME parameters GM MPA general model for measurements processing and analyses of TS MSD MPA model of the subject domain of measurements processing LE and analyses The types of the entities are set according to the real world objects that they relate to: i) target system entities (TSE) - entities related to the target system and its descrip- tion; ii) TMI system entities (TMIE) - entities related to the TMI system and its de- scription; iii) initial & processed measurements entities (IPME) entities related to initial and / or processed measurements and their descriptions; iv) system architecture entities (SAE) - entities related to the AIIS and its architecture; v) architectural framework entities (AFE) - entities related to architectural frameworks; vi) target system environment entities (TSEE) - entities related to target system environment; vii) measurements processing and analyses entities (MPAE) - entities related to meas- urements processing and analyses. Table 2. Types of the relations of the conceptual model Relation name Object Subject Description build based on ME ME models are built on the base of general models describe RE ME models describe objects of the real world operates in TS E target system functions in the environment used for data RE TMI real entities transfer data using TMI system transmission Systems used by AIIS for ME ME model entities of real objects are used for filling up filling up IS AIIS models of information space transfer TMI S M TMI systems transfer measurements define MSD GM model of the subject domain of measurements pro- MPA MPA cessing and analyses defines general model for meas- urements processing and analyses of TS is build accord- M M M MPA model of measurements is build according to model ing to for measurements processing and analyses of TS agree with MM M MPA model of measurements must agree with model for measurements processing and analyses of TS fills AIIS ME AIIS fills model of measurements; AIIS fills model of AIIS processes AIIS M AIIS processes measurements used for pro- M AIIS model for measurements processing and analyses of cessing and MPA TS is used in AIIS for dealing with measurements analyzes to be part of ME M AIIS dynamic model of AIIS and static model of AIIS part of AIIS model is built by mean AD AF AIIS architectural description of AIIS is build used archi- of AIIS tectural framework of AIIS Description of the conceptual model. A Target system (TS) is a system to be observed or investigated. Environment (E) is a context for TS, it defines conditions in which TS operates. A TS is used only for technical systems. For investigation of natural systems E concept is applied. A technical system is described by a target system model (M TS), for a natural system an environment model (M E) is build. A M TS is a set of models which describe TS. Models of end systems are built on the base of generalized models (GM TS and GM E). Results of parameters measurements of all systems are transferred to AIIS by means of telemetric IS (TMIS). A model of a TMIS (M TMIS) is a set of models which de- scribe the TMIS. M TMIS is based on a generalized model of telemetric systems (GM TMIS). Results of TS parameters measurements (M) are described in accordance with measurement models (M M), which are built on the base of generalized measurement model (GM M). Processing of results of measurements is executed in accordance with models for measurements processing and analyses of TS parameters (M MPA). Simi- lar to models of TS and models of TMIS models for measurements processing and analyses are based on generalized models (GM MPA). The list of methods and algo- rithms to be used in AIIS is defined by a subject domain model of measurements pro- cessing and analyses (MSD MPA). Architecture description of AIIS (AD AIIS) is made in accordance with standard [12] and hierarchy of the architectural descriptions of AIIS (HAD AIIS). HAD AIIS in- clude generalized architecture (GAM AIIS) and ontological (OM AIIS) models. Data, information and knowledge generated during the IS operation are stored in data, information and knowledge model (DIK model) that is the essential part of AIIS model (M AIIS). M AIIS contains both static and dynamic DIK that are located in static (SM AIIS) and dynamic (DM AIIS) models of AIIS model correspondingly. DM AIIS is used to save information about current state of the system and SM AIIS contains constant or rarely changed data. A term architecture framework (AF) defines 2 separate concepts. On one hand, AF is a tool for designing applied AIIS, on the other hand, it is “conventions, principles and practices for the description of architectures established within a specific domain of application and/or community of stakehold- ers” [12], or simply best practice. AF defines frameworks for building domain orient- ed IS. To design AF a hierarchy of architectural frameworks of AIIS (HAF AIIS) is used. An important part of AF is model MKB. MKB is a repository. All models are stored in MKB and are available to developers of one or different organization. 5 Stakeholders and scenarios of application Main AIIS stakeholders are end users, architects, sponsors and researchers. An end user is an expert of an applied subject domain that operates technical systems or is an expert who solves problems related to natural systems. An architect is an IT specialist or group of specialists who design AIIS with the use of AF. Sponsors fill the reposito- ry of the general models with new models or modify existing models. Researchers of natural systems and environment create new models / modify existing models that describe natural objects. Researchers develop new methods and algorithms. MKB is available for both researchers and developers. It is of primary importance for all architects and analyst. They can form their own models with the help of a toolbox. The toolbox is a GUI instrument with the help of which a user can populate MKB, choose necessary models from the list of available reference models, transform and aggregate them in order to receive necessary target model. Received models are used as an element of an architectural description of the target system. So, a user receives a validated model and minimizes risks of dealing with a wrong model and can save time for model development. The generalized scenario of the AIIS operation is shown in Fig. 2. An AIIS gathers results of measurements and use them for building measurement model. Activities initiated by the users are event based. A user forms requests for measurements pro- cessing and analyses. According to the request, model of AIIS is build. The model defines the structure and the content of DIK which are required to be received about the target natural or technical object as a result of the carried experiment. Then the model of measurements that reflect the expected raw data and results of its processing and analyses is created. For building a measurement model the models of target sys- tem and TMI system are required. AIIS operating mode Permanent activity TS TMI S M MM User M AIIS structure M M structure MM M AIIS request Fig 2. Generalized scenario of the AIIS operation Measurements processing is a linked sequence of transformations of raw data. As the transformations go, the measurement models and AIIS models are filled. Often for making transformations external information and knowledge about the target system is to be used. Table 3. Conceptual model projections Projection Id Description All Viewpoint AV integrates all view points; defines architectural context Systems Viewpoint SV considers a system as an aggregation of interacting subsys- tems; is used as a structural description of the system Functional (Capa- CV defines potential capabilities of a system for solving applied bility) Viewpoint problems Model Viewpoint ModV describes a system in terms of used models Object Viewpoint ObjV describes a system in terms of an object model Data, Information DIKV considers basic structures for data, information and and Knowledge knowledge representation and a set of specialized representa- Viewpoint tions dependent on the solved problems Behavior (Process) ВV considers a system as a set of working scenarios, executable Viewpoint (PR) activities, supported business processes Services Viewpoint SvcV considers a system as a set of services, describes functionality of а system at different levels of abstraction Architectural AV considers architecture of a system; processes of system archi- Viewpoint tecture design, systems development and support Platform ViewPoint PV considers a system as a set of platforms used for its imple- mentation Standards View- StdV considers actual technical standards, methodologies, instruc- point tions, restrictions with which the system complies 6 Conceptual model projections The list of projections contains views of the concept model that provide its overall detailing. They are coordinated with the standard [12]. The descriptions of the projec- tions are given in table 3. Levels of JDL model Infrastructure System Buisness Stakeholders services services services Level 0 Signal/ Feature Assessment Level 1 Entity Sponsor Assesement Level 2 Situation Assessment Level 3 End User Impact Assessement Level 4 Process Assessement Level 5 Architect Cognition Refinement Fig. 3. Service process JDL model As an example it is suggested to consider a service-oriented projection (Fig. 3). This model is most closely related to the implementation tasks. SvcV describes AIIS as set of services and business processes (BP). For the services 4 levels of functioning are defined. Infrastructure services ( f ) are usually elementary services. They are frag- ments of program code, i.e. java code. Each infrastructure service is associated with the certain level of JDL model. System services (y) are composite services. These services implement BP that assumes calling both Infrastructure and other System services. A System service as a rule is connected with 2 neighboring level of JDL model. Business services (b) are composite services which are described in terms of subject domain. They are to be formed on the base of system and other business ser- vices. External (high-level) (x) services that are targeted to support B2B interactions be also defined. They are similar to business services but without GUI. There are 4 different types of services: functional services (s) , DIK services (d ) , interpreters (engines) (e) and supporting services ( p) . Functional services imple- ment target procedures of processing and analyses. DIK services provide access to DIK resources. Engines are interpreters of scripting languages. Supporting services are services that support life cycle, security, etc. The services are distributed among levels of JDL model. Each business process (BP) in AIIS is considered as service, and each service can be realized as BP. In the context of SvcV business process is a sequence of calls of services described in BPEL style. BP can be either static or dynamic. The general idea of the proposed Service Process JDL (SPJDL) model is given in Fig.3. For Service description following notation can be suggested. A service is described as  Tlevel  jdl  , where T - type of services, level - level of services, jdl - level of JDL model; T  {s, d , e, p} , T  {s, d , e, p} , level  { f , y, b, x} , jdl [0; 5] . Different stakeholders work on different levels. An End user has an access only to Business Services. An Architect by the most part works with system and business services. Sponsors usually have rights to receive information about services in MKB and add new services into MKB. Сommonly they work with Infrastructure services. Developers and analyst have full access to all levels. 7 Conclusion In the paper an approach for solving a problem of analysis huge amounts of incom- ing data, in condition of existence of multiple obvious and hidden dependencies and external factors is discussed. The main idea of suggested approach to AIIS develop- ment is creation of AIIS conceptual model, which is to be used as meta model. Appli- cation of the conceptual model allows formalize procedures of creating models of applied systems of the domain level. Using transformations developers can form their own models by detailing projections of the conceptual model. The model saves time for model development by means of model reuse and allows receive reliable validated end models. They can be reused both in the frame of product lines and independent AIIS. The discussed approach provides support for end users who have to deal with measurement processing and analyses tasks in the contexts of solving applied prob- lems by means of reducing amount of data and increasing its utility. It is achieved due to building linked data and information space, using all available sources of infor- mation and knowledge and data enriching techniques, support machine learning pro- cedures regarding measurements processing. Procedures for measurements processing and analyses can be adapted in accordance with operative and historical context. It simplifies the tasks of exerts of data processing and analysis domain at the stage of system design The approach can be implemented using existing technologies in combination with semantic technologies. For creation of the conceptual model our practical experience in realization pro- jects in different subject domains such as monitoring state of complex technical sys- tems, intelligent GIS systems, medicine was used. All these systems were based on jdl model, detailed analysis show that they have many common features and use similar models. At present it is used in the project of medical data processing and analyses for Fed- eral Almazov North-West Medical Research Centre [13]. The project is executed in International laboratory "Information Science and Semantic Technologies" [14]. The detailed description is available on the web site of the Laboratory. Future activity is planned to be directed to population the conceptual model with new concrete models and adding new transformation features. References 1. National Technological Initiative. NeuroNet segment. CoBrain project (2015); http://government.ru/en/news/20118/ 2. IDC Research. 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