Towards Realization of Open Cloud Services Architecture for Data Service in Materials Field

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1 Journal of Computational Information Systems 10: 3 (2014) Available at Towards Realization of Open Cloud Services Architecture for Data Service in Materials Field Xin CHENG 1,2, Changjun HU 1,2,, Yang LI 1,2, Xin LIU 1,2 1 School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing , China 2 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing , China Abstract Against the new challenges for data management and service architecture due to the evolution of big data, the problems of scientific domain big data management, reuse and on-demand services must be solved. In this paper, we propose an Open Cloud Service Architecture (OCSA) based on the joint building of materials Virtual DataSpace (VDS), Requirement Space (RS) model and Behavior Space (BS) analysis, and discuss the theoretical concept and construction method of them. Based on the cloud computing supporting environment, we adopt the mechanisms of semantic mapping and dynamic evolution to support the idea of data first, on-demand service. The theoretical feasibility and supporting effect of OCSA have been effectively verified through the application of materials scientific data sharing service platform. Keywords: Open Cloud Services Architecture; Virtual DataSpace; Requirement Model; Behavior Analysis; Materials Data Service 1 Introduction In recent years, with the rapid growth of needs for the Internet data and related services, we are facing the new challenges of data management and service modeling. Data has been changed fundamentally in particular with the development of Web 2.0. The big data management issues [1, 2] are addressed in the aspect of domain scientific data. Especially in the materials field, there are some new data features such as massive, distributed, heterogeneous, complex association, dynamic changes, environmental correlation, and so on. Materials scientists find that it is difficult to deal with these new data features for realizing the e-science applications. Many issues are faced Project supported by the R&D Infrastructure and Facility Development Program (No. 2005DKA32800), the 2012 Ladder Plan Project of Beijing Key Laboratory of Knowledge Engineering for Materials Science (No. Z ), the Key Science-Technology Plan of the National Twelfth Five-Year-Plan of China (No. 2011BAK08B04), and the National Key Basic Research and Development Program (973 Program) (No. 2013CB329606). Corresponding author. address: hu.cj.mail@gmail.com (Changjun HU) / Copyright 2014 Binary Information Press DOI: /jcis9426 February 1, 2014

2 1112 X. Cheng et al. /Journal of Computational Information Systems 10: 3 (2014) as: how to efficiently manage the materials science big data; how to reuse and share the scientific data by deep mining data relationships; how to analyze the dynamic changes of domain scientific data and its relationships; how to proactively provide the individualized on-demand services for domain scientists; etc. Currently, the processing of large-scale data sets cannot catch the speed of data generation by modern scientific research. The traditional database management system can not meet the needs of management for the growing data. And the traditional service architecture which has the closed and rigid supporting model cannot satisfy the demands of new applications. Therefore, it is important to build a new data management model and new service architecture to support the realization of management for the large-scale data sets and the diversification of cloud service [3] applications. In this paper, our main object is to present a new data service architecture to manage the materials science data, and provide the effective data services. The rest of this paper is organized as follows. The related work about cloud computing and dataspace are introduced in Section 2. The Open Cloud Services Architecture (OCSA) is presented, and the key technologies involved in OCSA are elaborated in Section 3. The case study of OCSA in the materials field is described in Section 4. The brief conclusion and future work are given in Section 5. 2 Related Works Robert L Grossman et al. propose an Open Science Data Cloud (OSDC) [4] to support the analysis, processing and management of large-scale scientific data sets. Liangjie Zhang et al. introduce a Cloud Computing Open Architecture (CCOA) [5] to support the reusable and customizable of services based on seven architectural principles, realize the effective integration of Oriented Service Architecture (SOA) and virtualization in the ecological environment of cloud computing. K Keahey et al. [6] propose a method to construct scientific cloud platform infrastructure by using Nimbus CloudKit, the virtualization technology enable users to implement data-intensive scientific computing. Paul Watson et al. [7] use work flow to organize and manage the services and build the cloud platform Carmen based on the concept of pay-as-you-go to realize the data sharing, integration and analysis by the supporting of metadata. Pengcheng Xiong et al. propose a resource management system called SmartSLA [8] which can be applied to the cloud environment to support the intelligent management of cloud resources. M F Husain et al. [9] propose a massive data semantic storage and retrieval architecture based on the Hadoop technology to effectively improve the processing performance of storage and query. The researchers are trying to seek a new technology to deal with the new challenges of data management. The concept of dataspace was proposed by M Franklin et al. in 2005 [10]. The research about dataspace mainly includes two directions: one is the theory and algorithms study about model [11, 12], query [13, 14] and index [15] of dataspace; the other is about the technology research of personal data management and application. Currently, the dataspace research is still in its infancy, only a few prototype systems such as imemex [16] from ETH and Semex [17] from University of Washington are developed, and these two systems are mainly research the dataspace model, data storage and indexing, query processing, etc. As a new data management technology, the dataspace research is still in the initial stage. Moreover, the work combined the concept of dataspace with the cloud computing technology has been rarely done. In conclusion, there are many problems of domain data management that remain to be explored and resolved further.

3 X. Cheng et al. /Journal of Computational Information Systems 10: 3 (2014) Open Cloud Services Architecture (OCSA) Early as in 2006, Ibrahim Elsayed et al. [18] proposed an Open Grid Services Architecture (OGSA) by the basis of traditional grid five layers hourglass structure and Web Service technology. This architecture has combined the dataspace concept with the grid technology. It could effectively support the management of heterogeneous and distributed data. However, OGSA is unable to meet the data analysis, management and sharing with the increasingly prominent of big data problems. Based on this, we propose an Open Cloud Services Architecture (OCSA) to more emphasis the characteristics of virtualization and data resources allocated on demand. There are some similarities between OCSA and OGSA, such as the distributed computing, parallel processing, resolve the heterogeneous problem, support the resources sharing, and so on. However, OCSA is not the simple extension of OGSA. The significant differences between them are shown as Table 1. Core Focus OGSA Table 1: Compare OGSA and OCSA service as the core, emphasize resources sharing; resources scattered, applications focus workload is transferred to the available remote computing resources; focus parallel computing centralization demand OCSA data as the core, emphasize proprietary service; resources focus relatively, services scattered computing resources are converted in form to adapt the workload; focus a large number of separate transactional applications demand Treatment parallel job processing across physical machines virtualization processing, free scheduling of resources Extensibility difficult to automatically extend automatic/semi-automatic extend Reuse computing capability and data data and service Synergy working-level co-processing services and even data level (more flexible and diverse) Through the comparing and analyzing, it could be seen that OCSA has the following characteristics: 1) data-centric, i.e., support the Data as a Service (DaaS) ; 2) virtualization processing for data; 3) high-performance computing; 4) Virtual DataSpace (VDS), support the construction and evolution of data association; 5) Requirement Space (RS), support the construction and collaboration of services association; 6) Behavior Space (BS), support the behavior analysis of user habits, data dissemination, service evolution, and so on. Thus, we can define OCSA as follow. Definition 1 Open Cloud Services Architecture (OCSA) could be represented as a six-tuples, OCSA = (DRS, CSE, VDS, RS, BS, AIS). Where DRS denotes the data resource sets in the underlying physical layer of OCSA; CSE denotes the cloud computing supporting environment which support the virtualized processing by the Hadoop technology; VDS denotes the virtual dataspace which could manage and allocate the data resources on-demand by using the semantic mapping and dynamic evolution mechanism; RS denotes the requirement space which could build the requirement model and manage the services by combining the workflow technology; BS denotes the behavior space which could optimize the data services by behavior analysis; AIS denotes the application instance sets which are needed by the top level users of OCSA. The abstract framework of OCSA could be described as in Fig. 1. First of all, OCSA provides the virtual processing for the resource sets in data layer, and support the construction of data association in VDS and the construction of service association in RS based on CSE. It builds a global unified logical resource view through the semantic mapping and dynamic evolution of associated data in VDS. Meanwhile, establish the user requirements model for supporting the domain special services, and through the establishment of mapping between VDS and RS to

4 1114 X. Cheng et al. /Journal of Computational Information Systems 10: 3 (2014) open up the association among data, services and requirements. Furthermore, set up BS based on various types of behavior analysis in order to optimize the data services and meet the various user requirements of application layer. Fig. 1: Abstract framework of OCSA The detailed definition and analysis about DRS, CSE, VDS, RS and BS could be described as the following five parts. 3.1 Data acquisition from DRS The data resource sets (DRS) in the physical underlying of OCSA could be defined as a twotuples, DRS = (NDS, WNR), where NDS denotes the node data sets, WNR denotes the web network resources. We need adopt different technical schemes to achieve the data acquisition for these two types of data resources. For NDS, we use the interoperable interfaces based on data cross-domain sharing technology to realize the node data access. And for WNR, we adopt the network information intelligent crawl technology to get relevant data. They could be defined as follows. Definition 2 Node Data Sets (NDS) is a triples, NDS = (N i, DO ij, U ij ), i=1,2,,n, j=1,2,,m. Where n is the relevant nodes number; m is the number of data operation service provided by a certain node; N i denotes the node name; DO ij denotes the data operation of node N i ; U ij denotes the link URL of data operation DO ij in node N i. Definition 3 Web Network Resources (WNR) is a quadruple, WNR = (OD i, SS, U ij, AA), i=1,2,,n, j=1,2,,m. Where n is the number of crawling objective; m is the number of links about a certain crawling objective; OD i denotes the description about crawling objective; SS denotes the selected search strategy; U ij denotes the link URL of OD i ; AA denotes the selected analysis algorithms. There is the same parameter type link URL between NDS and WNR, therefore we can construct the data acquisition model (DAM) of OCSA as seen in Fig. 2. Through the combination of them, we can effectively obtain the massive data which distributed in different regions to solve the problems of big data distribution.

5 X. Cheng et al. /Journal of Computational Information Systems 10: 3 (2014) Fig. 2: Data acquisition model (DAM) 3.2 Virtualized processing in CSE The data resources updated frequently and have complexity types which acquired from the underlying physical nodes and web network of OCSA. Therefore the virtualization processing technology is adopted based on Hadoop to build the cloud computing supporting environment (CSE) as seen in Fig. 3. Hadoop could provide efficient distributed computing and storage supporting for the big data management because of its features of high transmission rate, high fault-tolerance, high reliability, low cost, high efficiency, and so on. The core technologies of Hadoop are HDFS and MapReduce. HDFS is the distributed file system of Hadoop, which supports the large file operation to realize the large-scale computing, storage and access. MapReduce is the distributed parallel programming model, which provides the Map function to decompose tasks and use the Reduce function to aggregate the distributed processing results into the end results. CSE provides the high performance computing and efficient store access supporting for OCSA, and then effectively meets the physical distribution, logical integration virtualized data processing requirements. Therefore, research the construction mechanism of Hadoop and optimize the programming algorithms of MapReduce, could significantly improve the data service of OCSA. Fig. 3: Cloud computing supporting environment (CSE) 3.3 Data organization and evolution in VDS Based on the high performance computing and efficient store access of CSE, we propose a Virtual DataSpace (VDS) technology to manage the data resources which are acquired from the underlying of OCSA. VDS is the sets of data, services and their relationships which related with the subjects and based on the supporting of virtualization. Compared with the traditional database and dataspace management mode, VDS has obvious technological advantages (as in Fig. 4. and Table 2), such as data first, more emphasis on the data associated mapping and dynamic evolution, more highlights the importance of service, and so on.

6 1116 X. Cheng et al. /Journal of Computational Information Systems 10: 3 (2014) Fig. 4: Comparison of DB, DS and VDS Table 2: Comparison of DB, DS and VDS DB DS VDS mode feature close, focus completion open, with pay-as-you-go feature data source only structured data structured, semi-structured, unstructured data data feature focus on data stability emphasis on data association and evolution data storage remote server each local data center service Web service (perpetual) retrieval services (centralization) cloud service (flexible dispersion) integration first model, after data weakening model, highlight data dilute model, emphasized data, on-demand service core application themes principal needs data services virtualization support virtualization processing of data services serviceability dynamic, diverse and personalized service requirement demand-led service coordination behavior optimize data services by behavior research, improve service initiative Studying how to construct VDS based on OCSA could satisfy the relevant needs of big data management and dynamic service improvement. From the macro level, the whole public virtual dataspace (P-VDS) could be understood as the sets of all the data and their relationships; and it could be defined as follow. Definition 4 Public Virtual DataSpace is a tuple, P-VDS = (ADS, ARS), where ADS is all the data set, ARS is all the relationship set. VDS usually associates to the related subjects, it is equivalent to the subset of public virtual dataspace, i.e. P-VDS = S-V DS v, v=1,2,,v. Where V is the number of sub-vds; S-V DS v denotes the sub virtual dataspace, its subject-related data set is the subset of ADS, its subjectrelated data relationships set is the subset of ARS. So the sub-vds mainly include four parts: subject, service, data and relationship, it could be defined as follow. Definition 5 Sub-VDS is a four-tuples, S-VDS = (P i, S j, DS ij, DRS ij ), i=1,2,,i, j=1,2,,j. Where I is the number of subjects; J is the number of services; P i denotes the owner of this sub-vds, i.e. the subject; S j denotes the service which is supported by this sub-vds; DS ij denotes the data sets related with P i and S j ; DRS ij denotes the data relationship sets related with P i and S j. Combined with the Definition 4, it is not the corresponding relation among the number of subjects, services and sub-vds, one subject might joined several services to several sub-vds, one service might exists in several subjects related sub-vds. Thus the number of sub-vds should

7 X. Cheng et al. /Journal of Computational Information Systems 10: 3 (2014) meet data ranges: V [I, I J]. The data sets of sub-vds could be expressed as: DS ij = DS V = DEd, d=1,2,,d, where D is the number of data entities in this data sets, DE d is the data entity, it could be defined as follow. Definition 6 Data Entity is a five-tuples, DE = (DATA name, pro, value, CD D P, CD D S ). Where DATA data, pro, value is the triad of name, property and value for this data entity; CD D P is the correlation degree between data and subject; CD D S is the correlation degree between data and service. The data relationship set of sub-vds could be expressed as: DRS ij =DRS V = DRE r1, r1= 1,2,,R1, where R1 is the number of data relationship entities in this data sets, DRE r1 is the data relationship entity, it could be defined as follow. Definition 7 Data Relationship Entity is a five-tuples, DRE=(TN, CD D, DES DE 1 DE 2 DE d, O L, W DR OL ). Where TN is the type name of the data relationship entity; CD D is the correlation degree of data; DES DE 1 DE 2 DE d is the data entity sets involved this data relationship entity, d is the number of involved data entities, DE d is the d-th involved data entity; O L is the local ontology which the data relationship entities belong to; W DR OL is the weight of data relationships in the corresponding local ontology. The local ontology set could be expressed as: OS Lij =OS LV = O Lo, o=1,2,,o, where O is the number of local ontologies in sub-vds (DS V ) related with P i and S j, O Lo is the local ontologies in sub-vds (DS V ), it could be defined as follow. Definition 8 Local Ontology is a triples, O L = (ON L, S-VDS v, W OL OG ). Where ON L is the name of this local ontology; S-VDS v is the sub-vds which the local ontologies belonged to; W OL OG is the weight of local ontology in the global ontology. The global ontology is set up by the local ontology set of each sub-vds, it could be expressed as: O G = OS Lij = OS LV = O Lo = (O Lo1 +O Lo2 + +O LoV ), therefore, the number of local ontology in global ontology could be calculated as: O = o 1 +o 2 + +o V = o v, v=1,2,,v, where V is the number of sub-vds in the global ontology, o v is the number of local ontologies in the v-th sub-vds. Therefore, we can construct VDS which supports the semantic mapping and dynamic evolution as the following steps: Step 1 Design the different semantics wrappers to wrap and transform the heterogeneous data; Step 2 Initialization of VDS: determine the domain core concepts, initially set S j that demanded by P i, and then select the interested DS ij and DRS ij to build the initial S-VD S v which is related to P i ; Step 3 Expansion of VDS: determine CD D P and CD D S of the newly included data according to the initial definition of data set, and put the related data into the suitable S-VDS v ; Step 4 Data evolution: amend CD D P and CD D S based on the frequency degree of data access and the supporting of valid algorithms, and then continuously optimize the DS ij of S- VDS v ; Step 5 Relationship evolution: continuously correct the CD D of data and improve DRS ij by combining the initial definition of data relationship set and the related optimization of data entities DE 1 DE 2 DE d which are involved in the data relationship set; Step 6 Local ontology mapping: build the mapping M RL : DRS ij OS Lij, then correct the W DR OL of relationship entities in the belonging local ontology based on the evolution of data

8 1118 X. Cheng et al. /Journal of Computational Information Systems 10: 3 (2014) relationships; Step 7 Global ontology mapping: build the mapping M LG : OS Lij O G, then correct the W OL OG of local ontologies in the global ontology, thereby improve P-VDS which is integrated by lots of S-VDS v. From the building process as in Fig. 5, we can see that VDS could effectively solve the problems of association complex and dynamic changing for big data in OCSA. Fig. 5: The building process of VDS 3.4 Requirements modeling in RS VDS has realized the flexible and personalized service, furthermore, we propose a Requirement Space (RS) to lead the collaborative work and improve the initiative of service in OCSA. VDS provides the bottom-up data leading to satisfy the service demands, in contrast, RS provides the top-down needs leading to optimize the service data. Co-ordinate VDS and RS into OCSA could complement each other and achieve more abundant cloud data services. RS is the set of data services and their relationships which related to the user requirements. It mainly includes five features: 1) demand first ; 2) emphasize the association and evolution of services; 3) support the service semantic collaboration, has higher initiative of services; 4) mapping with VDS by use the services as a link; 5) support the research of behavior evolution analysis. RS could be defined as follow. Definition 9 Requirement Space is a six-tuples, RS = (U i, RQ k, SS ik, SRS ik, DS ik, DRS ik ), i=1,2,,i, k=1,2,,k. Where I is the number of users; K is the number of requirements; U i is the user of RS; RQ k is the requirement included in RS; SS ik is the service set which is needed by users; SRS ik is the service relationship set; DS ik is the data set which is needed by users; DRS ik is the data relationship set. The modeling process of RS could be described as in Fig. 6. Firstly, adopt the method of OWL-S to describe, register, publish and obtain the services in SS ik and the service relationships in SRS ik to build the association mapping of service semantic. Secondly, find the regularity of synergistic combination by using the workflow technology, and then integrate the services of two or more based on the ideology of Mashup to efficiently achieve the service semantic collaboration. Finally, build the mapping of VDS and RS based on the establishment of semantic association between the S ik in RS and S j in VDS to get through the communication between the upper demands and the underlying data. Based on this, optimize the quality of data service to realize the personalized service integration and diversified sharing collaboration in OCSA.

9 X. Cheng et al. /Journal of Computational Information Systems 10: 3 (2014) Fig. 6: The modeling process of RS 3.5 Behavior analysis in BS Based on the data management in VDS and the services coordination in RS, we could optimize and improve the data services in OCSA by behavior analysis technology. Thereby we propose a Behavior Space (BS) as a new concept and method in the OCSA. BS is the scope of the dynamic evolutionary track and its correlation analysis about the related members. The members of BS mainly include user, requirement, data, service, relationship, threat and behavior itself. It forms a relatively stable system of relations based on the interaction of members, and then builds a basic mode for BS. There are different types of behavior analysis in BS, such as the user behavior analysis, the data dissemination analysis, the service evolution analysis, the requirement evolution analysis, the threatening behavior analysis, the relationship evolution analysis, and so on. The supporting model of BS as Fig. 7 shows that it has the characteristics of interaction, real-time performance, effects propagation, and so on. With the relevant members as a link, OCSA supports the effectively construction of mappings among VDS, RS and BS, then provides the optimization of data services. The statistical analysis and probability comparison based on the behavior characteristics could provide the user-interested and personalized service recommendation. And the behavior prediction mechanism constructed by the behavior analysis could support the active service and user-friendly design of OCSA. Fig. 7: The supporting model of BS 4 Materials Scientific Data Sharing Service Platform For the analysis and validation of OCSA, we developed a Materials Scientific Data Sharing Service Platform based on the Materials Domain Cloud Services Architecture (MDCSA) and achieved the practical applications in OCSA. As shown in Fig. 8, this platform has integrated the massive data resources into twelve categories, such as ferrous materials, composite materials, organic polymer materials, etc. The data resource set aggregated various types of materials relevant information such as grades, properties, processing, manufacturer, etc., and covered many types of materials scientific data such as tables, XML documents, images, etc. At present, the

10 1120 X. Cheng et al. /Journal of Computational Information Systems 10: 3 (2014) platform has totally collected nearly six hundred thousand data resource items, and the amount of data is still growing rapidly. Fig. 8: Materials Domain Cloud Services Architecture (MDCSA) Extract and transform the data obtained from each node and network based on the cloud computing supporting environment, and then initially built several basic sub-vds such as the property combination query sub-vds, failure range judgment sub-vds, etc. Meanwhile, initially built various types of RS according to the users demands, for example, the keyword search RS, materials selection RS, life prediction RS, and so on. In addition, according to the variety behavior analysis, initially formed several categories of basic BS model such as user habits BS, service evolution prediction BS, etc. Build the mapping between VDS and RS based on the data associated evolution of VDS and the service associated combination of RS to get through the barriers between the upper layer user demands and the lower layer data construction. And then use the various types of analysis in BS to further optimize the data service for providing a wide variety of featured application services of material scientific domain in the upper layer of MDCSA. The specific application service instances such as keywords fuzzy query

11 X. Cheng et al. /Journal of Computational Information Systems 10: 3 (2014) service, building materials failure warning service, pipeline full life cycle services, and so on. The whole platform building process instances model is shown in Fig. 9. Fig. 9: Platform building process instances model 5 Conclusions and Future Work For the realization of big data management, reuse and on-demand services, this paper proposed an Open Cloud Service Architecture (OCSA), elaborated the involved key technologies, and then verified the feasibility and superiority of OCSA by studying the related application case in materials field. The building of OCSA has combined with the cloud computing, VDS, RS, BS, ontology semantic, etc., it has the scientifically rigorous theory support and flexible extensive application prospects. The future work will further develop the framework of OCSA, depth study the dynamic evolution algorithm of VDS, improve the theoretical research of RS and BS, then deep mining the relevant building technology and realization measures, moreover, add the technical support for real-time and uncertainty in OCSA. References [1] C. Lynch, Big data: How do your data grow?, Nature 455 (2008) [2] D. Howe, M. Costanzo, et al., Big data: The future of biocuration, Nature 455 (2008) [3] A.I. Yong, X. ZHANG, K.E. Jie, et al., Application of Information Resource View on Cloud Computing Service, Journal of Computational Information Systems 9(2) (2013) [4] Robert L. Grossman, Y. Gu, J. Mambretti, et al., An overview of the Open Science Data Cloud, in: Proc. 19th ACM International Symposium on High Performance Distributed Computing, 2010, pp [5] L.J. Zhang, Q. Zhou, CCOA: Cloud Computing Open Architecture, in: Proc. IEEE International Conference on Web Services, 2009, pp [6] K. Keahey, R. Figueiredo, J. Fortes, T. Freeman, M. Tsugawa, Science Clouds: Early Experiences in Cloud Computing for Scientific Applications, Cloud Computing and Its Applications (2008)

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