Integration of Heterogeneous Data Sources in Smart Grid based on Summary Schema Model

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1 Integration of Heterogeneous Data Sources in Smart Grid based on Summary Schema Model Foroogh Sedighi Department of Computer Engineering Niroo Research Institute Tehran, Iran Mahshid Helali Moghadam Department of Computer Engineering Niroo Research Institute, University of Kashan Tehran, Iran Abstract The modern infrastructure of electric grid known as smart grid is based on advanced communication and information technologies. The concept of data integration between heterogeneous data sources of smart grid reveals some challenges. The focus of this paper is on data integration as one of the major challenges in IT infrastructure of smart grid. In this paper, a semantic-based hierarchical integration model based on Summary Schema Model (SSM) has been proposed. The proposed model aims to optimize the cost of query in distributed heterogeneous databases. We represent how implementing a multidatabase system based on a Summary Schema Model would help to address and resolve data integration issues in IT infrastructure of smart grid. The analysis shows that the SSMbased integration model reduces the query response time compared to classic schema integration model. IndexTerms smart grid, heterogeneous databases, data integration, summary schema model I. INTRODUCTION The smart grid is defined as the integration of power, communications and information technologies in order to enhance the traditional electric power system into a modern and more intelligent one [1].The smart grid consists of a set of interrelated systems that are different in terms of their infrastructure, information system that they use, and also the geographic regions where they are located [1, 2]. The focus of information technology interoperability is to provide interoperability and data integration among developed systems that are totally independent and isolated from each other in the IT infrastructure of smart grid [1, 3]. In fact, in this context there are several issues such as site autonomy, differences in data representation including differences in names, structure and, abstraction that should be taken into consideration [4, 5]. In other words, there are several heterogeneous software systems in this context that are represented as heterogeneous distributed databases. Because of autonomy and heterogeneity of the software systems in IT infrastructure of smart grid, data integration becomes a big challenge. In the context of multidatabases, there are different solutions for data integration which can be categorized as following: Global Schema (Schema Integration): In this approach a global schema will be created and is going to be used as a mean in order to resolve global functions and provide a uniform view of shared data to locate and access distributed heterogeneous data sources [6-9]. Multidatabase Language: In this approach global schema does not exist. The system is not user friendly and if the user needs data, should directly communicate with the site to get data [8]. Summary Schema Model: Transparency and a kind of global schema exist in this model. The creation of the global schema can be automatic. The size of meta-data is also much smaller comparing to Schema Integration approach [8, 10]. One of the key requirements to achieve benefits of smart grid is data integration [4]. In this paper, the main contribution is to propose a proper solution for data integration of heterogeneous databases in IT infrastructure of smart grid. One of the most important domains of the smart grid is distribution domain which includes a high variety of heterogeneous software systems. Considering the variety and heterogeneity of software systems in distribution domain of smart grid, the main focus of this paper is put on data integration in IT infrastructure of distribution domain and a hierarchical integration model based on Summary Schema Model has been proposed. The rest of this paper is organized as follow. Section II describes the works proposed by different authors in the context of data integration in the smart grid. Section III provides an overview of IT infrastructure of distribution domain and briefs the need of interoperability between involved systems and data integration in this domain. In section IV, the principles of data integration in multiple heterogeneous databases are presented. Section V introduces the proposed hierarchical integration model and discusses about the query processing in this model. In section VI, a discussion on the reasons to apply this model for data integration in IT infrastructure of distribution domain and related analysis is provided. Finally, Section VII concludes the paper and presents the future works. II. RELATED WORK Modern information and communication technology (ICT) play an important role in smart grid. So that, database systems which are essential parts of ICT are also indispensable components of smart grid. Storing, retrieving and processing data in smart grid can all be done through using database systems. Since, autonomy and heterogeneity are the main /16/$ IEEE 88

2 factors of these systems, thus generating an appropriate information integration model has become a challenging issue. In [2] and [11] several challenging issues of information integration in the scope of smart grid were addressed and explained. The challenges of data integration and accessibility of information in applications from heterogeneous distributed organizations were investigated. In [12] a framework based on cloud computing for information management in smart grid called Smart-Frame was presented. The intention was to generate a hierarchical structure which provides flexibility, scalability and also security. In [13] the context of cloud computing for information management in smart grid was used. It proposed a paradigm called Cloud Service based SG Information Management (CSSGIM). CSSGIM improves the level of information integration and also reduces the operational cost. A framework for the development of a distributed real-time event-based software infrastructure by designing an event-driven middleware was provided in [3]. The intention was to achieve interoperability among heterogeneous systems and simplify data access in the context of smart grid. In addition to solutions mentioned above, some have gone through data integration in smart grid through using the semantic context of shared data. In [14] (S 2 )In-architecture for semantic-based integration of data services was proposed. The goal was integration of multiple types of data services in smart grid to achieve automation in the communication processes. A cloud-based software platform using a semantic information integration pipeline was built in [15]. The pipeline provided semantic annotation for data context enrichment. By this, data from different sources and different types could be managed. The focus of [16] was also on data integration in the smart grid context. For this purpose, an open data model representation integrated with SOA (Service Oriented Architecture) was proposed. XML and web services were used in this model for data exchange in a uniform and standard format. So, despite heterogeneity of components in smart grid, the level of interactivity improved. In another work [17], the role of SOA in data integration in the context of smart grid was discussed. This paper aims at providing an appropriate solution for data integration of heterogeneous systems which suites the smart grid context and meets the requirements such as keeping the local autonomy of systems, scalability, decreasing the average of search time which are of great importance to information integration in IT infrastructure of smart grid. III. IT INFRASTRUCTURE OF DISTRIBUTION DOMAIN IN SMART GRID As mentioned before, there is a variety of software systems which are represented as data sources in IT infrastructure of distribution domain. This context is a peer to peer platform which is fully distributed and each data source has its own database management. In the following, a brief description of major software applications involved in IT infrastructure of distribution domain [1] along with the need to provide interoperability among these applications is provided. Distribution Management System (DMS): Performs and manages all control functions and operations related to distribution grid. Outage Management System (OMS): Manages the records of outages and is useful for detecting and analyzing the outages. Geographical Information System (GIS): Contains geographical data of the distribution electrical components. AMI Head End System (AHE): A head-end system receives the stream of meter data brought back to the utility through the AMI. Asset Management System: Manages the equipment and the applied operations on them. Each software system in IT infrastructure of smart grid can be presented as a preexisting data source which is managed by a database management system. These databases run on heterogeneous platforms and may be completely different from each other in terms of their structure, local constraints, information representation that they use, and also their geographic regions. In other words, autonomy (degree of a database independency) and heterogeneity of databases are two fundamental factors that should be taken into account in finding a solution to provide information integration and interoperability between heterogeneous databases. IV. APPROACHES IN DESIGNING MULTIDATABASE SYSTEMS There are three different approaches to design and implement a multidatabase system. In the following a brief description of each approach is provided. A. Global Schema Approach There are two different phases involving in data integration in Global Schema approach. These phases are as follows: Schema integration: In the Global Schema approach a global schema is built on top of local schemas. Result integration: when a query is submitted to the global schema, it is decomposed to a set of subqueries posed to local resources. Integration of partial results generated from sub-queries is to combine them in order to provide the result in a uniform format. In this approach, for each local database any heterogeneity among local schemas in terms of semantic and syntactic should be resolved. In Global Schema approach, transparency exists and user does not need to be knowledgeable about the whole system [8, 9]. The challenges related to this approach are as following [18]: To handle semantic of data and form a global schema, human should be involved. Any change at local schema should also be reflected in global schema. This means massive computation. A huge database of gathered data from different local databases will be generated to form a global schema. B. Multidatabase Language Approach In this approach, global schema does not exist. The intention is to solve some of the discussed challenges related to the Global Schema approach such as the amount of required knowledge, computation requirements, storage and etc. In 89

3 multidatabase language approach, defining a common name space or language for all schemas is essential. User makes a request based on a common language which is knowledgeable about. Since the local site may not be familiar with the language, a translator has been put at each site. The translator translates the common language to the local language. Transparency of the first approach does not exist here. This means that if the user needs specific information, should directly communicate with the sites that contain that information. So, user should be knowledgeable about the whole system [8, 18]. C. Summary Schema Model Summary Schema Model is a solution that does not carry difficulties of the first two approaches. The main intention in this approach is to automate the identification of entities that are similar in semantics but have different representations. Summary Schema Model is a hierarchical relational structure. To build this structure, by using a standard dictionary, the linguistic relationships among existing terms in each node will be defined. At each level, the upper node captures semantics of the information at the lower level [8, 19]. The advantages of using this approach are listed as follows [20]: Transparency exists in this approach. So, user does not need to know where data is located. The approach is user friendly because the system is dealing with semantics. Performing global integration is automated. Generated meta-data in this approach is much smaller than global schema approach. Less storage is required. V. PROPOSED HIERARCHICAL INTEGRATION MODEL In this section, a hierarchical integration model based on Summary Schema Model has been proposed. The main intention of applying this model is to integrate the independent software systems of IT infrastructure of smart grid with no change in any of them and thus save their investment in existing different parts of their systems. The proposed model is shown in Fig.1. It has been applied to some parts of the main heterogeneous databases in the IT infrastructure of distribution domain. As shown in Fig.1, at the lowest level, there are physical databases that are to be integrated. In Fig.1, we have five data sources entitled DMS, OMS, GIS, AHE and Asset Management System with relations entitled Equipe-Loc, OutageRecord, Add-Component, Meter- Event and Device-App_Function and the contents of these relations. Summary schema is built on top of the local databases based on the thesaurus. Each node in this model contains the semantic contents of its lower level nodes. Each node consists of a set of access terms form schema. The relation between access terms can be shown as synonyms, hypernyms (terms with more abstraction) and hyponyms (terms with less abstraction). By mapping access terms at lower levels to their related hypernyms, a summary schema node will be generated [8, 10]. The operational flow of our proposed model is provided as follows: In the 1 st level of the proposed model, we have local databases with the characteristics shown in Table 1. In the 2 nd level, we have head nodes with the attributes presented in Table 2. In the 3 rd level, there are head nodes with the attributes shown in Table 3. In the 4 th level, we have head nodes with the attributes presented in Table 4. A. Query Processing In the Proposed Model In classic Global Schema approach, the query submitted to the global schema, is decomposed to a set of sub-queries posed to local resources. Then, partial results generated from local resources should be combined to generate the final result. Therefore, query processing in Global Schema approach consists of two phases of query decomposition and partial results integration. Traversing the integration steps is a highly computation-intensive process [18, 6]. Query processing in Summary Scheme Model follows a completely different scheme. Once a query is submitted to a node in SSM model, if the location of required data and access terms that would be used is clear, it is considered as an explicit query and will be processed locally. Otherwise, it is known as an inexplicit or imprecise query. When an imprecise query is initiated, it will be connected to a node. Each node contains some information. The content of query and content of the information in the requested node will be compared semantically. In case of semantic similarity, it is clear that real relevant data exists in the lower level of the requested node. So, the query should move down in the hierarchy till it reaches the physical data. In case of no similarity between content of the query and content of the node where the request is initiated, the request should move up in the hierarchy till it reaches the root. Finally, if no matches is found in the root, it can be concluded that required information does not exist in that information system [8].The query processing in SSM model does not involve the computation-intensive phases of query decomposition and results integration, therefore the total cost of query resolution is expected to be less than the Global Schema approach. The cost of a query in SSM can be measured as the number of message exchanges between the nodes until the query is resolved. The expected value for the cost of query in SSM can be measured as follows: ) (1) Where C i is the cost of query in node "i" and 1/ number of nodes is the probability of selecting node "i". In SSM, calculating the cost of query is location-dependent. This means that, the cost of query depends on where the query has been initiated. Assume a sample hierarchy built on some of the heterogeneous data sources in IT infrastructure of distribution domain of smart grid, presented in Fig. 2 and Fig. 3. It contains 20 nodes including 10 local database schemas and 10 summary schemas. Assume two different plans for an imprecise query which the origin node for the first plan is node 3 and the origin node for the second plan is node 8. The destination node for both plans is node

4 KEY: { Schema Content }, DB = database name, R = Relation name, A = Attribute name In Fig. 2 and Fig. 3, the search and return path for an imprecise query initiating in two different nodes (plan A and B) have been shown. The cost of query (number of exchanged messages) for plan A is 12 while the cost of query for plan B is 7. It can be shown that the upper bound of C i (the cost of query at node i) is 4n where "n" is the length of semantic hierarchy. Figure 4 displays the length of search path for an imprecise query initiating in different origin nodes in the sample hierarchy. It is assumed that node 10 is the destination node. As shown in Fig. 4, the closer to the destination node the query initiates, the search path would be shorter. Consequently the cost of query would become less. By considering the processing time of messages in nodes and the messages transfer time between nodes, we can calculate the query response time for node "i" as follows. (2) Where Data Transfer Time is the required time to transfer the messages in both search and return paths. The data transfer time for each message is calculated as follows: (3) Assuming that the processing time of each node is 0.1s, the size of query is 1KB, the size of the result is 1MB, and the bandwidth of the connecting link between nodes is 100Mb/s. Figure 5 represents the query response time initiating in different origin nodes. Fig. 1 The proposed model on some parts of five heterogeneous data sources VI. DISCUSSION The IT infrastructure of smart grid is a geographically distributed environment comprising a variety of highly heterogeneous data sources with local autonomies. The data communication between distributed systems in the smart grid is based on a fully distributed peer-to-peer platform and each system has its own data resource and can communicate with other systems. In the smart grid, there is a large amount of distributed data in various data sources known as software systems which need to be integrated to provide the information management and interoperability. In the IT infrastructure of smart grid the scalability is an important factor which facilitates the possibility of joining new software applications to the system domain. The need of reducing query response time in data communication between different heterogeneous systems is also an important requirement that should be considered. Regarding this issues, this smart grid environment requires an efficient solution for data integration to resolve information management and functional interoperability. The proposed integration model can be a proper solution to meet the mentioned requirements of the smart grid environment. Since, it is shown that it benefits from following advantages: Reduces the total storage requirements. Provides the possibility of automatic creation of the global schema Reduces the search path and consequently query response time 91

5 Keep the autonomy of the local databases In addition to above features, according to the conformity between the hierarchical structure of the proposed model and the inheritance relation hierarchy of the concepts in CIM, we TABLE 1 the characteristics of the leaf nodes Node 1.A (DMS) Node 1.B (OMS) Node 1.C (GIS) Node 1.D (AHE) Node 1.E (Asset Management System) Node 3.A Node 3.B Position Disconnector Breaker Protected Switch Region Reason Type of Outage Start and End Date and Time Cartesian coordinate Substation Switch Load Breaker Meter ID Power Up/Down Date and Time Site Device ID Date and Time of TABLE 3 the 3 rd level nodes Geographical Information Conducting Equipment Container Equipment PlacementSite Incident Operation Incident Time Operation Time Testimony Meter Identifier Description Represents the position of the distribution components in the electrical dispatching grid Represents a specific outage in a specific region caused by a specific reason along with its start and end date and time. Represents the Cartesian coordinate of components. Represents the occurrence of a power-up/down event in a specific meter and at a specific date and time. Represents the type of function which has been applied on a specific device at a specific date and time. Hypernym Location Equipment Area Event Event Time Symptom Device can take advantage of using CIM concepts to build a thesaurus for the hierarchical structure of the parts related to electrical entities. Node 2.A Node 2.B Node 2.C Node 4.A TABLE 2 the 2 nd level nodes Position Region Disconnector Breaker Protected Switch Substation Region Site Type of Outage Power Up/Down Start and End Date and Time Date and Time Reason Meter ID Device ID Date and Time of TABLE 4 the 4 th level nodes Location Area Equipment Event Event Time Symptom Hypernym Geographical Information Conducting Equipment Container Equipment Placement Incident Incident Time Testimony Meter Identifier Identifier Operation Operation Time Hypernym Situation Asset Occurrence Time Fundamental Cause Fig. 2 The search path in plan A Search path: 3, 12, 17, 20, 19, 16, 10 Return path: 10, 16, 19, 18, 17, 12, 3 Search path: 8, 15, 19, 16, 10 Return path: 10, 16, 15, 8 Fig. 3 The search path in plan B VII. CONCLUSION In this study, a hierarchical data integration model for heterogeneous data sources in IT infrastructure of distribution domain in smart grid was proposed. The proposed integration model is a Summary Schema Model which was adapted to the data context of smart grid in distribution domain. The proposed integration model uses the semantic relations hierarchy to build an abstract view of shared data. Regarding the use of CIM as interoperable and common concepts for applications implementation in smart grid, the SSM-based model is able to use the CIM hierarchical relations to build the integration 92

6 hierarchy. Therefore, the proposed approach provides a standard integration model based on the common concepts of the smart grid. This paper proposes a SSM-based integration model which is flexible to the local autonomy issues of the heterogeneous systems and is highly scalable. It reduces significantly the required storage for storing the global schema. The analysis of cost of query and query processing time shows that the proposed SSM-based integration model improves the cost of query and the query processing time compared to the classic global schema approach. Extending the proposed data integration model to support the security issues including data access controls and authentication issues in smart grid may be the directions for future studies. Fig. 4 The search path for different origin nodes (Destination node "10") REFERENCES [1] D. G. Photovoltaics and E. Storage, "IEEE Guide for Smart Grid Interoperability of Energy Technology and Information Technology Operation with the Electric Power System (EPS), End-Use Applications, and Loads," [2] Z. Fan, P. Kulkarni, S. Gormus, C. Efthymiou, G. Kalogridis, M. Sooriyabandara, et al., "Smart grid communications: Overview of research challenges, solutions, and standardization activities," IEEE Communications Surveys & Tutorials, vol. 15, pp , [3] E. Patti, A. L. A. Syrri, M. Jahn, P. Mancarella, A. Acquaviva, and E. Macii, "Distributed software infrastructure for general purpose services in smart grid," IEEE Transactions on Smart Grid, vol. 7, pp , [4] A. Ipakchi, "Implementing the smart grid: enterprise information integration," in GridWise Grid-Interop Forum, 2007, pp [5] Z. Aung, "Database systems for the smart grid," in Smart Grids, ed: Springer, 2013, pp [6] A. Calì, D. Calvanese, G. De Giacomo, and M. Lenzerini, "Data integration under integrity constraints," in Seminal Contributions to Information Systems Engineering, ed: Springer, 2013, pp [7] C. Y. Ko, "Three approaches to a multidatabase system," in Proceedings of Philippine Somputer Science Congress, [8] S. Ngamsuriyaroj, A. R. Hurson, and T. F. Keefe, "Authorization model for summary schemas model," inproceedings of International Database Engineering and Applications Symposium, 2002, pp [9] M. T. Özsu and P. Valduriez, Principles of distributed database systems: Springer Science & Business Media, [10] Y. Jiao and A. R. Hurson, "Mobile agents and energy-efficient multidatabase design," in 18th International Conference on Advanced Information Networking and Applications, 2004, pp Fig. 5 The query response time for different origin nodes (Destination node 10 ) [11] Z. Bojkovic and B. Bakmaz, "Smart grid communications architecture: a survey and challenges," Proceedings of the 11th International coriference on Applied Computer and Applied Computational Science (ACACOS), pp , [12] J. Baek, Q. H. Vu, J. K. Liu, X. Huang, and Y. Xiang, "A secure cloud computing based framework for big data information management of smart grid," IEEE transactions on cloud computing, vol. 3, pp , [13] X. Fang, S. Misra, G. Xue, and D. Yang, "Managing smart grid information in the cloud: opportunities, model, and applications," IEEE network, vol. 26, pp , [14] S. Rohjans, "A standard-compliant ICT-architecture for semantic data service integration in smart grids," in Innovative Smart Grid Technologies, IEEE PES, 2013, pp [15] Y. Simmhan, S. Aman, A. Kumbhare, R. Liu, S. Stevens, Q. Zhou, et al., "Cloud-based software platform for big data analytics in smart grids," Computing in Science & Engineering, vol. 15, pp , [16] R. Malarvizhi and S. Kalyani, "SOA based open data model for information integration in smart grid," in Fifth International Conference on Advanced Computing, 2013, pp [17] G. A. Pagani and M. Aiello, "Service Orientation and the Smart Grid state and trends," Service Oriented Computing and Applications, vol. 6, pp , [18] M. Bright, A. R. Hurson, and S. Pakzad, "Automated resolution of semantic heterogeneity in multidatabases," ACM Transactions on Database Systems (TODS), vol. 19, pp , [19] M. Ongtang, A. R. Hurson, Y. Jiao, and T. E. Potok, "Agentbased transactions management for mobile multidatabase," in Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2007), 2007, pp [20] J. B. Lim and A. R. Hurson, "Transaction processing in a mobile, multi-database environment," Multimedia Tools and Applications, vol. 15, pp ,