Rivero L.Encyclopedia of database technologies and applications.2006

Belussi, A., Bertino, E., & Catania, B. (1998). An extended algebra for constraint databases. IEEE Transactions on Knowledge and Data Engineering, 10(5), 686-705.

Booch, G., Rumbaugh, J., & Jacobson, I. (1998). The unified modeling language user guide. Essex, UK: Addison-Welsley Longman.

Bordogna, G., Pasi, G., & Lucarella, D. (1999). A fuzzy object-oriented data model for managing vague and uncertain information. International Journal of Intelligent Systems, 14, 623-651.

Bostan, B., & Yazici, A. (1998). A fuzzy deductive object-oriented data model. Proceedings of the IEEE International Conference on Fuzzy Systems (Vol. 2, pp. 1361-1366).

Bourret, R. (2001). XML and databases. Retrieved from http://www.rpbourret.com/xml/XMLAnd Databases. htm

Buckles, B. P., & Petry, F. E. (1982). A fuzzy representation of data for relational database. Fuzzy Sets and Systems, 7(3),213-226.

Chang, Y. L., Chen, S., Chen, C. C., & Chen, I. (2000). Workflow process definition and their applications in e-commerce. Proceedings of International Symposium on Multimedia Software Engineering (pp. 193-200).

Chen, G. Q., & Kerre, E. E. (1998). Extending ER/EER concepts towards fuzzy conceptual data modeling. Proceedings of the 1998 IEEE International Conference on Fuzzy Systems (Vol. 2, pp. 1320-1325).

Chen, P. P. (1976). The entity-relationship model: Toward a unified view of data. ACM Transactions on Database Systems, 1(1), 9-36.

Conrad, R., Scheffner, D., & Freytag, J. C. (2000). XML conceptual modeling using UML. Lecture Notes in Computer Science, 1920, 558-571.

Damiani, E., Oliboni, B., & Tanca, L. (2001). Fuzzy techniques for XML data smushing. Lecture Notes in Computer Science, 2206, 637-652.

Dong, Y., & Goh, A. (1998). An intelligent database for engineering applications. Artificial Intelligence in Engineering, 12, 1-14.

Dong, Y., Chee, C. F. Y., He, Y., & Goh, A. (1997). Active database support for STEP/EXPRESS models.

Journal of Intelligent Manufacturing, 8(4), 251-261.

Dzbor, M. (1999). Intelligent support for problem formalisation in design. Proceedings of the 3rd IEEE Conference on Intelligent Engineering Systems.

Engineering Information Modeling in Databases

Eastman, C. M., & Fereshetian, N. (1994). Information models for use in product design: A comparison. Com- puter-Aide Design, 26(7), 551-572.

George, R., Srikanth, R., Petry, F. E., & Buckles, B. P. (1996). Uncertainty management issues in the object-oriented data model. IEEE Transactions on Fuzzy Systems, 4(2), 179-192.

Goh, A., Hui, S. C., Song, B., & Wang, F. Y. (1994). A study of SDAI implementation on object-oriented databases.

Computer Standards & Interfaces, 16, 33-43.

Goh, A., Hui, S. C., Song, B., & Wang, F. Y. (1997). A STEP/ EXPRESS to object-oriented databases translator. International Journal of Computer applications in Technology, 10(1/2), 90-96.

Guiffrida, A., & Nagi, R. (1998). Fuzzy set theory applications in production management research: A literature survey. Journal of Intelligent Manufacturing, 9, 39-56.

ISO IS 10303-1 TC184/SC4: Product Data Representation and Exchange-Part 1: Overview and Fundamental Principles, International Standard, 1994.

ISO IS 10303-1 TC184/SC4: Product Data Representation and Exchange-Part 11: The EXPRESS Language Reference Manual, International Standard, 1994.

Kim, W., Banerjee, J., Chou, H. T., & Garza, J. F. (1990). Object-oriented database support for CAD. ComputerAided Design, 22(8), 521-550.

Krebs, T., & Lührsen, H. (1995). STEP databases as integration platform for concurrent engineering. Proceedings of the 2nd International Conference on Concurrent Engineering (pp. 131-142).

Kuper, G., Libkin, L., & Paredaens, J. (2000). Constraint databases. Heidelberg, Germany: Springer-Verlag.

Kusiak, A., Letsche, T., & Zakarian, A. (1997). Data modeling with IDEF1X. International Journal of Computer Integrated Manufacturing, 10(6), 470-486.

Lee, D. W., & Chu, W. W. (2000). Constraints-preserv- ing transformation from XML document type definition to relational schema. Lecture Notes in Computer Science, 1920, 323-338.

Lee, M. L., Lee, S. Y., Ling, T. W., Dobbie, G., & Kalinichenko, L. A. (2001). Designing semistructured databases: A conceptual approach. Lecture Notes in Computer Science, 2113, 12-21.

Liu, M. C. (1999). On CAD databases. Proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering (pp. 325-330).

Engineering Information Modeling in Databases

Ma, Z. M. (2004). Imprecise and uncertain engineering information modeling in databases: Models and formal transformations. In P. van Bommel (Ed), Transformation of knowledge, information and data: Theory and applications (pp. 153-175). Hershey, PA: Infosci.

Ma, Z. M., Lu, S. Y., & Fotouhi, F. (2003). Conceptual data models for engineering information modeling and formal transformation of EER and EXPRESS-G. Lecture Notes in Computer Science, 2813, 573-75.

Ma, Z. M., Zhang, W. J. & Ma, W. Y. (2001). Fuzzy data type modeling with EXPRESS. Proceedings of the ASME 2001 International Design Engineering Technical Conferences and the Computers and Information in Engineering Conference, September 28-October 2, Salt Lake City, Utah.

Ma, Z. M., Zhang, W. J., & Ma, W. Y. (2002). Extending IDEF1X to model fuzzy data. Journal of Intelligent Manufacturing, 13(4), 295-307.

Ma, Z. M., Zhang, W. J., Ma, W. Y., & Chen, G. Q. (2000). Extending EXPRESS-G to model fuzzy information in product data model. Proceedings of the 2000 ASME Design Engineering Technical Conference.

Mannisto, T., Peltonen, H., Soininen, T. & Sulonen, R. (2001). Multiple abstraction levels in modeling product structures. Date and Knowledge Engineering, 36(1), 55-78.

McKay, A., Bloor, M. S., & de Pennington, A. (1996). A framework for product data. IEEE Transactions on Knowledge and Data Engineering, 8(5), 825-837.

Medina, J. M., Pons, O., Cubero, J. C., & Vila, M. A. (1997). FREDDI: A fuzzy relational deductive database interface. International Journal of Intelligent Systems, 12(8), 597-613.

Mili, F., Shen, W., Martinez, I., Noel, Ph., Ram, M., & Zouras, E. (2001). Knowledge modeling for design decisions. Artificial Intelligence in Engineering, 15,

153-164.

Oh, Y., Hana, S. H., & Suhb, H. (2001). Mapping product structures between CAD and PDM systems using UML.

Computer-Aided Design, 33, 521-529.

OMG. (2003). Unified modeling language (UML). Retrieved from http://www.omg.org/technology/documents/formal/uml.htm

Posselt, D., & Hillebrand, G. (2002). Database support for evolving data in product design. Computers in Industry, 48(1), 59-69.

Prade, H., & Testemale, C. (1984). Generalizing database relational algebra for the treatment of incomplete or uncer- E tain information. Information Sciences, 34, 115-143.

Samaras, G., Spooner, D., & Hardwick, M. (1994). Query classification in object-oriented engineering design systems. Computer-Aided Design, 26(2), 127-136.

Saygin, Y., & Ulusoy, O. (2001). Automated construction of fuzzy event sets and its application to active databases. IEEE Transactions on Fuzzy Systems, 9(3), 450-460.

Schenck, D. A., & Wilson, P. R. (1994). Information modeling: The EXPRESS way. Cambridge, UK: Oxford University Press.

Sebastian, H. J., & Antonsson, E. K. (1996). Fuzzy sets in engineering design and configuration. Norwell, MA: Kluwer Academic Publishers.

Seligman, L., & Rosenthal, A. (2001, June). XML’s impact on databases and data sharing. IEEE Computer, 34(6), 5967.

Shaw, N. K., Bloor, M. S., & de Pennington, A. (1989). Product data models. Research in Engineering Design, 1, 43-50.

Surjanto, B., Ritter, N., & Loeser, H. (2000). XML content management based on object-relational database technology. Proceedings of the First International Conference on Web Information Systems Engineering (Vol. 1, pp. 70-79).

Van Gyseghem, N., & Caluwe, R. D. (1998). Imprecision and uncertainty in UFO database model. Journal of the American Society for Information Science, 49(3), 236252.

Wizdom Systems. (1985)., U.S. Air Force ICAM manual: IDEF1X. Naperville, IL.

Xiao, R. G., Dillon, T. S., Chang, E., & Feng, L. (2001). Modeling and transformation of object-oriented conceptual models into XML schema. Lecture Notes in Computer Science, 2113, 795-804.

Yazici, A., Soysal, A., Buckles, B. P., & Petry, F. E. (1999). Uncertainty in a nested relational database model. Data & Knowledge Engineering, 30, 275-301.

Young, R. E., Giachetti, R., & Ress, D. A. (1996). A fuzzy constraint satisfaction system for design and manufacturing. Proceedings of the 5th IEEE International Conference on Fuzzy Systems (Vol. 2, pp. 1106 -1112).

Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3-28.

Zhang, W. J., & Li, Q. (1999). Information modeling for made-to-order virtual enterprise manufacturing systems.

Computer-Aided Design, 31(10), 611-619.

Zimmermann, H. J. (1999). Practical Applications of

Fuzzy Technologies. Norwell, MA: Kluwer Academic

Publishers.

Zvieli, A., & Chen, P. P. (1986). Entity-relationship modeling and fuzzy databases. Proceedings of the 1986 IEEE International Conference on Data Engineering, 320-327.

KEY TERMS

Conceptual Data Modeling of Engineering Information: Using conceptual data models to implement the data modeling of engineering information. The conceptual data models for engineering data modeling include some special conceptual data models for industry, such as EXPRESS/STEP and IDEF1X, and some traditional conceptual data models, such as ER/EER and UML.

Data Modeling: Implementing data management in engineering information systems with information technology and, in particular, database technology. The complex data semantics and semantic relationships are described in data modeling.

Engineering Information Modeling in Databases

Database Models: Conceptual data models and logical database models.

Engineering Information Systems: The information systems used to manage the information in data and knowledge-intensive engineering applications and to implement various engineering activities.

EXPRESS/STEP: To share and exchange product data, the Standard for the Exchange of Product Model Data (STEP) is being developed by the International Organization for Standardization (ISO). EXPRESS is the description methods of STEP and can be used to model product design, manufacturing, and production data.

IDEF1X: IDEF1X, one of IDEF tools developed by Integrated Computer-Aided Manufacturing (ICAM), is a formal framework for consistent modeling of the data necessary for the integration of various functional areas in computer integrated manufacturing (CIM).

Logical Database Modeling of Engineering Information: Using logic database models to implement the data modeling of engineering information. The logical database models for engineering data modeling include some traditional database models such as relational database model and object-oriented database model and some special, hybrid, and extended database models.

Shin Parker

University of Nebraska at Omaha, USA

Zhengxin Chen

University of Nebraska at Omaha, USA

IMPORTANCE OF ENSURING SERIALIZABILITY IN MOBILE ENVIRONMENTS

Data management in mobile computing has emerged as a major research area, and it has found many applications. This research has produced interesting results in areas such as data dissemination over limited bandwidth channels, location-dependent querying of data, and advanced interfaces for mobile computers (Barbara, 1999). However, handling multimedia objects in mobile environments faces numerous challenges. Traditional methods developed for transaction processing (Silberschatz, Korth & Sudarshan, 2001) such as concurrency control and recovery mechanisms may no longer work correctly in mobile environments. To illustrate the important aspects that need to be considered and provide a solution for these important yet “tricky” issues in this article, we focus on an important topic of data management in mobile computing, which is concerned with how to ensure serializability for mobileclient data caching. New solutions are needed in dealing with caching multimedia data for mobile clients, for example, a cooperative cache architecture was proposed in Lau, Kumar, and Vankatesh (2002). The particular aspect considered in this article is that when managing a large number of multimedia objects within mobile client-server computing environments, there may be multiple physical copies of the same data object in client caches with the server as the primary owner of all data objects. Invalidaccess prevention policy protocols developed in traditional DBMS environment will not work correctly in the new environment, thus, have to be extended to ensure that the serializability involving data updates is achieved in mobile environments. The research by Parker and Chen (2004) performed the analysis, proposed three extended protocols, and conducted experimental studies under the invalid-access prevention policy in mobile environments to meet the serializability requirement in a mobile client/ server environment that deals with multimedia objects. These three protocols, referred to as extended serverbased two-phase locking (ES2PL), extended call back

locking (ECBL), and extended optimistic two-phase locking (EO2PL) protocols, have included additional attributes to ensure multimedia object serializability in mobile client/ server computing environments. In this article, we examine this issue, present key ideas behind the solution, and discuss related issues in a broader context.

BACKGROUND

In a typical client-server computing architecture, there may exist multiple physical copies of the same data object at the same time in the network with the server as the primary owner of all data objects. The existence of multiple copies of the same multimedia object in client caches is possible when there is no data conflict in the network. In managing multiple clients’ concurrent read/write operations on a multimedia object, no transactions that accessed the old version should be allowed to commit. This is the basis of the invalid-access prevention policy, from which several protocols have been proposed. The purpose of these protocols is to create an illusion of a single, logical, multimedia data object in the face of multiple physical copies in the client/server network when a data conflict situation arises. When the server becomes aware of a network-wide data conflict, it initiates a cache consistency request to remote clients on behalf of the transaction that caused the data conflict. Three well-known invalid-access prevention protocols are Server-based Two-Phase Locking (S2PL), Call-Back Locking (CBL), and Optimistic Two-Phase Locking (O2PL).

In order to extend these policies to the mobile environment, we should understand that there are four key constraints of mobility which forced the development of specialized techniques, namely, unpredictable variation in network quality, lowered trust and robustness of mobile elements, limitations on local resources imposed by weight and size constraints, and concern for battery power consumption (Satyanarayanan, 1996). The inherent limitations of mobile computing systems present a challenge to the traditional problems of database management, especially when the client/server communication is

Figure 1. CBL failure analysis tree in a mobile environment

Object A

Conclusion: CBL needs a version number to detect obsolete replicas.

unexpectedly severed from the client site. The standard policy does not enforce the serializability to the mobile computing environment. Transactions executing under an avoidance-based scheme must obey the Read-Once Write-All (ROWA) principle, which guarantees the correctness of the data from the client cache under the CBL or the O2PL protocol. The standard CBL and O2PL protocols cannot guarantee the currency of the mobile clients’ cache copies or prevent serializability violations when they reconnect to the network. Figures 1 illustrates how error conditions (appearing toward the end of the figure) arise after mobile clients properly exit the client application when the traditional CBL protocol is used.

FUNDAMENTAL ISSUES AND APPROACHES TO DEALING WITH THESE ISSUES

In order to extend invalid-access prevention policy protocols to mobile environments, there are three fundamental issues that need to be addressed for mobile-client multimedia data caching, namely:

•to transform multimedia objects from databases’ persistent data type to the clients’ persistent data type;

•to handle client-server communication for multimedia objects; and

•to deal with the impact of mobility, particularly to deal with the case when the client-server communication is unexpectedly severed from the client site.

Research work from various authors (Breitbart et al., 1999; Franklin, Carey & Livny, 1997; Jensen & Lomer, 2001; Pacitti, Minet & Simon, 1999; Shanmugasundaram et al., 1999; Schuldt, 2001) have contributed to the inves-

Table 1. Extended invalid-access prevention policy

tigation of aspects related to ensuring serializability of data management. Based on these studies, Parker and Chen (2004) have conducted a more recent research to deal with the three issues mentioned above and developed algorithms to achieve extended invalid-access prevention protocols. The basic ideas of this research are summarized below.

First, in order to prevent the serializability failure scenario described above, we summarize important features of the extended invalid-access prevention policy protocols for the mobile client/server environments that guarantee the serializability. As shown in Table 1, an X denotes an attribute under the standard invalid-access prevention policy, while a bold-face X as an additional attribute under the extended invalid-access prevention policy. The revised algorithms for extended invalid-ac- cess prevention policy protocols are developed based on these considerations.

As an example of these attributes, here we take a brief look at the important role of the page table. To detect or avoid invalid-accesses from all transactions, all clients and the server each need to keep a separate table to detect or avoid data conflict situations. For clients, page tables are the current inventories of their cached multimedia objects. For the server, a page table is the information

about their active constituent clients to detect or avoid data conflicts in the network. Figure 2 depicts a proper page table procedure for logical invalidations to deal with serializability problems through page table consistency check.

EXTENDING TRADITIONAL PROTOCOLS:

BASIC IDEA AND RESULTS OF EXPERIMENTS

To illustrate the basic ideas involved in extending standard protocols, let us take a look at the case of extended O2PL algorithm with dynamic replication. In its original form, the O2PL protocol defers the write declaration until the end of a transaction’s execution phase. Dynamic replication is a natural choice for the O2PL because when the server issues a write lock after multicasting to all cached remote clients of the same object, the server already has an updated object at hand from the committing client. Just before the server commits the new object to the database with an exclusive lock, there are two copies of the new version object in the network, the server’s binary array variable and the local client’s new-version cache copy and only one primary copy of the old version object in the database.

The correctness criterion in the replicated database is one-copy serializability (Holiday, Agrawal & Abbadi, 2002) under the ROWA principle where write operations must write to all copies before the transaction can complete. This is accomplished via the server’s multicast transmission to a subset of active clients after the primary newversion copy is safely stored in the database.

The primary copy of an object is with the server, and the replicas are at client caches for read transactions. For write transactions, the primary copy is at the transaction’s

originating site temporarily. After commit operations, however, the server becomes the primary site, and the originating client becomes one of the replicated sites. The server then multicasts the replica to remote clients with the previous version object. When a client downloads an object explicitly, a local lock is given automatically, but the end user can release the local lock manually. Local locks will not be automatically given after dynamic replications.

To enforce the network-wide unique object ID, the server application will verify the uniqueness of the file name at the insert transaction, and the client application will verify that the file name is not altered at the commit transaction as an early abort step.

RESULT OF EXPERIMENTS ON EXTENDED PROTOCOLS

The three extended protocols have been implemented, and comparative studies through experiments have been conducted. Below is the result of an experiment where identical transactions of four clients are used, each with two multimedia objects (one small size and the other 15 times as large). Table 2 summarizes the number of messages clients sent to the server, total kilobytes of the messages, the number of the server aborts, and the abort rate which is the percentage of aborts from the entire number of messages clients sent to the server. Any dynamic replications do not count toward the messages sent since clients do not send any messages to the server for them. All client-initiated abort messages, such as the permit abort in the ECBL or the lock abort in the ES2PL, are counted toward the MESSAGE, not the ABORT.

Experiments have shown that extended invalid-ac- cess prevention policy algorithms enforce a guaranteed serializability of multimedia objects in RDBMS applications under a mobile client/server environment. As for

the pros and cons of each extended algorithm, we have the following general observations. Extended S2PL protocol brings the lowest number of client messages to the server but at the highest server abort rate leaving the network with multiple versions. Extended CBL protocol with invalidation carries the highest number of client messages sent to the server and a moderate server abort rate in the expense of reliability. Extended O2PL protocol with replication offers a moderate number of client messages sent to the server with the lowest server abort rate that may make it desirable for most applications.

FUTURE TRENDS

Due to its importance for data management in a mobile environment, techniques for ensuring serializability in dealing with multiple copies of multimedia objects should be further explored. New solutions are needed for existing problems in new environments, and new problems emerge, demanding solutions, as well. In this article, we have deliberately focused on a well-selected specific topic to show the need for an in-depth study of dealing with mobility in databases involving multimedia objects. However, the methodology used here can be extended to many other topics in regard to data management in a mobile computing environment. The key to success in such studies lies in a good understanding of important features of mobile environments, as well as inherent limitations of involved resources in such environments.

In addition, ensuring serializability has implications beyond guaranteeing correctness of transaction execution in a mobile environment. For example, based on the work reported above, Parker, Chen, and Sheng (2004) further identified four core areas of issues to be studied in database-centered mobile data mining, with an emphasis on issues related to DBMS implementation such as query processing and transaction processing. Other as- pects related to data mining techniques and distributed or mobile (or even pervasive) computing environments have

also been explored (e.g., Kargupta & Joshi, 2001; Lim et al., 2003; Liu, Kargupta & Ryan, 2004; Saygin & Ulusoy, 2002).

There are many other database issues related to mobile computing, such as location-dependent queries (Dunham, Helal & Balakrishnan, 1997; Seydim, Dunham & Kumar, 2001; Yan, Chen & Zhu, 2001). In addition, many issues related to mobile computing can be examined in a more general context of pervasive computing (or ubiquitous computing). Satyanarayanan (1996, 2001) discussed several important issues and future directions on pervasive computing. A wide range of data management aspects should be explored in the future, with the following as sample topics:

•Infrastructure for mobile computing research

•User interface management for pervasive devices

•Data models for mobile information systems

•Mobile database management and mobility-aware data servers

•Mobile transaction and workflow management and models

•Data and process migration, replication/caching and recovery

•Moving objects and location-aware data management

•Adaptability and stability of pervasive systems in ever-changing wireless environments

•Quality of service (QOS) mechanism for mobile data management

CONCLUSION

As noted earlier, handling multimedia objects in mobile environments faces numerous challenges. Traditional methods developed for transaction processing such as concurrency control and recovery mechanisms may no longer work correctly in mobile environments. In this article, we have focused on a specific issue to ensure

serializability for mobile-client data caching. We have explained why the traditional approaches need to be revised and demonstrated the basic idea of extended approaches. Extending from this particular study, we have also discussed related issues in a more general perspective. As indicated in the Future Trends section , there are numerous challenging issues to be resolved in the near future.

REFERENCES

Barbara, D. (1999). Mobile computing and databases: A survey. IEEE Transactions on Knowledge and Data Engineering, 11(1), 108-117.

Breitbart, Y., Komondoor, R., Rastogi, R., Seshadri, S., & Silberschatz., A. (1999). Update propagation protocols for replicated databases. Proceedings of the 1999 ACM SIGMOD Conference (pp. 97-108).

Dunham, M.H., Helal, A., & Balakrishnan, T. (1997). Mobile transaction model that captures both the data and movement behavior. Mobile Networks and Applications, 2(2),149-162.

Franklin, M.J., Carey, M.J., & Livny, M. (1997). Transactional client-server cache consistency: Alternatives and performance. ACM Transactions on Database Systems, 22(3), 315-363.

Holiday, J., Agrawal, D., & Abbadi, A. (2002). Disconnection modes for mobile databases. Wireless Networks, 8(4), 391-402.

Jensen, C.S., & Lomer, D.B. (2001). Transaction timestamping in temporal databases. Proceedings of the 27th International Conference on Very Large Data Bases

(pp. 441-450).

Kargupta, H., & Joshi, A. (2001). Data mining “to go”: Ubiquitous KDD for mobile and distributed environments. Tutorial Notes of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4.1-4.78).

Lau, W.H.O., Kumar, M., & Vankatesh, S. (2002). A cooperative cache architecture in support of caching multimedia objects in MANETs. Proceedings of the WoMMoM 02

(pp. 56-63).

Lim, E.-P., Wang, Y., Ong, K.-L., & Hwang, S.-Y. (2003, July). In search of knowledge about mobile users. Center for Advanced Information Systems at Nanyang Technological University, Singapore. Retrieved February 5, 2005,

Liu, K., Kargupta, H., & Ryan, J. (2004, January). Distributed data mining bibliography. University of Maryland Baltimore County. Retrieved February 5, 2005, from http:/ /www.cs.umbc.edu/~hillol/DDMBIB/ddmbib.pdf

Pacitti, E., Minet, P., & Simon, E. (1999). Fast algorithms for maintaining replica consistency in lazy master replicated databases. Proceedings of the 25th International Conference on Very Large Data Bases (pp. 126-137).

Parker, S., Chen, Z., & Sheng, E. (2004). Ensuring serializability for mobile data mining on multimedia objects. Proceedings of the CASDMKM 2004 (pp. 90-98).

Parker, S., & Chen, Z. (2004). Extending invalid-access prevention policy protocols for mobile-client data caching. Proceedings of the ACM SAC 2004 (pp. 1171-1176).

Satyanarayanan, M. (1996). Mobile information access.

IEEE Personal Communications, 3(1), 26-33.

Satyanarayanan, M. (2001). Pervasive computing: Vision and challenges. IEEE Personal Communications, 8, 10-17.

Saygin, Y., & Ulusoy, Ö. (2002). Exploiting data mining techniques for broadcasting data in mobile computing environments. IEEE Transactions on Knowledge Data Engineering, 14(6), 1387-1399.

Schuldt, H. (2001). Process locking: A protocol based on ordered shared locks for the execution of transactional processes. Proceedings of the 20th ACM SIGMOD SIGACT SIGART Symposium on Principles of Database Systems (pp. 289-300).

Seydim, A.Y., Dunham, M.H., & Kumar, V. (2001). Location dependent query processing. Proceedings of the 2nd ACM International Workshop on Data Engineering for Wireless and Mobile Access (pp. 47-53).

Shanmugasundaram, S., Nithrakashyap, A., Sivasankaran, R., & Ramamritham, K. (1999). Efficient concurrency control for broadcast environments. Proceedings of the 1999 ACM SIGMOD International Conference in Management of Data (pp. 85-96).

Silberschatz, A., Korth, H.F., & Sudarshan, S. (2001).

Database system concepts (4th ed.). New York: WCB McGraw-Hill.

Yan, J., Chen, Z., & Zhu, Q. (2001). An approach for query optimizing in a mobile environment. Proceedings of the Joint Conference on Information Systems (JCIS 2001)

(pp. 507-510).

KEY TERMS

Call-Back Locking (CBL): CBL is an avoidancebased protocol that supports inter-transactional page caching. Transactions executing under an avoidancebased scheme must obey the read-once write-all (ROWA) replica management approach, which guarantees the correctness of data from the client cache by enforcing that all existing copies of an updated object have the same value when an updating transaction commits.

Data Management for Mobile Computing: Numerous database management issues exist in mobile computing environments, such as resource management and system support, representation/dissemination/management of information, location management, as well as others. Various new techniques for cache management, data replication, data broadcasting, transaction processing, failure recovery, as well as database security, have been developed. Applications of these techniques have been found distributed mobile database systems; mobile information systems; advanced mobile computing applications; and the Internet. Yet there are still many other issues need to be dealt with, such as the problem described in this article.

Invalid Access Prevention Policy: The invalid-access prevention policy requires that in order to manage multiple clients’ concurrent read/write operations in the client/server architecture, no transactions that access stale multimedia data should be allowed to commit. In general, there are two different approaches to achieve this policy. The detection-based (lazy) policy ensures the validity of accessed multimedia data, and the avoidance-based (eager) policy ensures that invalid multimedia data is preemptively removed from the client caches.

Multimedia Database: A particular challenge for a multimedia database is the ability of dealing with multimedia data types. Retrieval of structured data from databases is typically handled by a database management system (DBMS), while retrieval of unstructured data from databases requires techniques developed for information retrieval (IR). (A survey on content-based retrieval for multimedia databases can be found in Yoshitaka and Ichikawa, A survey on content-based retrieval for multi-

Ensuring Serializability for Mobile-Client Data Caching

media databases, IEEE Transactions of Knowledge and Data Engineering, 11(1), pp. 81-93, 1999.) Yet the rigid resource requirement demands more advanced techniques in dealing with multimedia objects in a mobile computing environment.

Optimistic Two-Phase Locking (O2PL): This is avoid- ance-based and is more optimistic about the existence of data contention in the network than CBL. It defers the write intention declaration until the end of a transaction’s execution phase. Under the ROWA protocol, an interaction with the server is required only at client cache-miss or for committing its cache copy under the O2PL. As in CBL, all clients must inform the server when they erase a page from their buffer so that the server can update its page list.

Pervasive Computing (or Ubiquitous Computing):

Pervasive computing “has as its goal the enhancing of computer use by making many computers available throughout the physical environment, but making them effectively invisible to the user” (Mark Weiser, Hot topics: Ubiquitous computing, IEEE Computer, October 1993, p. 000). Pervasive computing is the trend towards increasingly ubiquitous, connected computing devices in the environment. As the result of a convergence of advanced electronic (particularly, mobile wireless) technologies and the Internet, pervasive computing is becoming a new trend of contemporary technology.

Serializability: Serializability requires that a schedule for executing concurrent transactions in a DBMS is equivalent to one that executes the transactions serially in a certain order.

Server-Based Two-Phase Locking (S2PL): The S2PL uses a detection-based algorithm and supports intertransaction caching. It validates cached pages synchronously on a transaction’s initial access to the page. Before a transaction is allowed to commit, it must first access the primary copies from the server on each data item that it has read at the client. The new value must be installed at the client if the client’s cache version is outdated. The server is aware of a list of clients who requested locks only, and no broadcast is used by the server to communicate with clients.

ENTERPRISE APPLICATION INTEGRATION (EAI) TECHNOLOGY

As long as businesses only have one enterprise application or back-end application system, there is no need to share data with any other system in the company. All data that has to be managed is contained within one back-end application system. However, as businesses grow, more back-end application systems find their way into their information technology infrastructure, managing different business data. These back-end application systems are not independent of each other; in general they contain similar business data or are part of the same business processes. This requires their integration for exchanging data between them. The technology that allows this is called enterprise application integration (EAI) technology. EAI technology is able to connect to back-end application systems in order to retrieve and to insert data. Once connected, EAI technology supports the definition of how extracted data is propagated to back-end application systems, solving the integration problem.

BACKGROUND

Typical examples of back-end application systems that are deployed as part of a company’s IT infrastructure are an enterprise resource planning (ERP) system and a manufacturing resource planning (MRP) system. In the general case, different back-end application systems store potentially different data about the same objects, like customers or machine parts. For example, a part might be described in an ERP as well as an MRP system. The reason for the part being described in two different back-end application systems is that different aspects of the same part are managed. In fact, this means that the not necessarily equal representation of the object exists twice, once in every system. If there are more than two systems, then it might be very well the case that the same object is represented several times. Any changes to the object have to be applied in every system that contains the object. And, since this cannot happen simultaneously in the general case, during the period of changing, the same object will be represented differently until the changes have been applied to all representations in all back-end application

systems. Furthermore, in most cases there is no record of how many systems represent the same object. It might very well be the case and actually often it is the case that a change is not applied to all objects because it is not known which back-end application system has a representation of the object in the first place.

In summary, the same object can be represented in different back-end application systems, the updates to an object can cause delays and inconsistencies, and locations of object representations can be unknown.

A second use case is that precisely the same object is replicated in different back-end application systems. In this case the update of the object in one system has to be applied to all the other systems that store the same object. The objects are replicas of each other since all have to be updated in the same way so their content is exactly the same. Only when all the objects are updated are they consistent again and the overall status across the backend application systems is consistent again.

A third use case is that applications participate in common business processes. For example, first a part is being purchased through the ERP system, and upon delivery it is entered and marked as available in the MRP system. The business process behind this is consisting of several steps, namely, purchase a part, receive the part, make the part available, and so on. In this case the backend application systems do not share common data, but their state depends on the progress of a business process, and it has to update the back-end application systems accordingly. In this sense they share a common business process, each managing the data involved in it.

All three use cases, while looking quite different from each other, have to be implemented by companies in order to keep their business data consistent. EAI technology (Bussler, 2003; Hohpe & Woolf, 2003) allows companies to accomplish this as it provides the necessary functionality as described next.

Enterprise Application Integration

Technology

Enterprise application integration technology addresses the various scenarios that have been introduced above by providing the required functionality. In the following, the different functionalities will be introduced step by step. First, EAI technology provides a reliable communication