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29.1 Mobile Databases I919 29.1.2 Characteristics of Mobile Environments As we discussed in the previous section, the characteristics of mobile computing include high communication latency, intermittent wireless connectivity, limited battery life, and, of course, changing client location. Latency is caused by the processes unique to the wireless medium, such as coding data for wireless transfer, and tracking and filtering wireless signals at the receiver. Battery life is directly related to battery size, and indirectly related to the mobile device's capabilities. Intermittent connectivity can be intentional or unintentional. Unintentional disconnections happen in areas wireless signals cannot reach, e.g., elevator shafts or subway tunnels. Intentional disconnections occur by user intent, e.g., during an airplane takeoff, or when the mobile device is powered down. Finally, clients are expected to move, which alters the network topology and may cause their data requirements to change. All of these characteristics impact data management, and robust mobile applications must consider them in their design.' To compensate for high latencies and unreliable connectivity, clients cache replicas of important, frequently accessed data, and work offline, if necessary. Besides increasing data availability and response time, caching can also reduce client power consumption by eliminating the need to make energy-consuming wireless data transmissions for each data access. On the other hand, the server may not be able to reach a client. A client may be unreachable because it is dozing-in an energy-conserving state in which many subsystems are shut down or because it is out of range of a base station. In either case, neither client nor server can reach the other, and modifications must be made to the architecture in order to compensate for this case. Proxies for unreachable components are added to the architecture. For a client (and symmetrically for a server), the proxy can cache updates intended for the server. When a connection becomes available, the proxy automatically forwards these cached updates to their ultimate destination. As suggested above, mobile computing poses challenges for servers as well as clients. The latency involved in wireless communication makes scalability a problem. Because latency due to wireless communications increases the time to service each client request, the server can handle fewer clients. One way servers relieve this problem is by broadcasting data whenever possible. Broadcast takes advantage of a natural characteristic of radio communications, and is scalable because a single broadcast of a data item can satisfy all outstanding requests for it. For example, instead of sending weather information to all clients in a cell individually, a server can simply broadcast it periodically. Broadcast also reduces the load on the server, as clients do not have to maintain active connections to it. Client mobility also poses many data management challenges. First, servers must keep track of client locations in order to efficiently route messages to them. Second, client data should be stored in the network location that minimizes the traffic necessary to access it. Keeping data in a fixed location increases access latency if the client moves "far away" from it. Finally, as stated above, the act of moving between cells must be 3. This architecture is based on the IETF proposal in IETF(1999) with comments by Carson and Macker (1999). 920 I Chapter 29 Emerging Database Technologies and Applications transparent to the client. The server must be able to gracefully divert the shipment of data from one base station to another, without the client noticing. Client mobility also allows new applications that are location-based. For example, consider an electronic valet application that can tell a user the location of the nearest restaurant. Clearly, "nearest" is relative to the client's current position, and movement can invalidate any previously cached responses. Upon movement, the client must efficiently invalidate parts of its cache and request updated data from the database. 29.1.3 Data Management Issues From a data management standpoint, mobile computing may be considered a variation of distributed computing. Mobile databases can be distributed under two possible scenarios: 1. The entire database is distributed mainly among the wired components, possibly with full or partial replication. A base station or fixed host manages its own database with a DBMS-like functionality, with additional functionality for locating mobile units and additional query and transaction management features to meet the requirements of mobile environments. 2. The database is distributed among wired and wireless components. Data management responsibility is shared among base stations or fixed hosts and mobile units. Hence, the distributed data management issues we discussed in Chapter 24 can also be applied to mobile databases with the following additional considerations and variations: • Data distribution and replication: Data is unevenly distributed among the base stations and mobile units. The consistency constraints compound the problem of cache management. Caches attempt to provide the most frequently accessed and updated data to mobile units that process their own transactions and may be disconnected over long periods. • Transaction models: Issues of fault tolerance and correctness of transactions are aggra- vated in the mobile environment. A mobile transaction is executed sequentially through several base stations and possibly on multiple data sets depending upon the movement of the mobile unit. Central coordination of transaction execution is lack- ing, particularly in scenario (2) above. Moreover, a mobile transaction is expected to be long-lived because of disconnection in mobile units. Hence, traditional ACID properties of transactions (see Chapter 19) may need to be modified and new transaction models must be defined. • Query processing: Awareness of where data is located is important and affects the cost/ benefit analysis of query processing. Query optimization is more complicated because of mobility and rapid resource changes of mobile units. The query response needs to be returned to mobile units that may be in transit or may cross cell boundaries yet must receive complete and correct query results. • Recovery and fault tolerance: The mobile database environment must deal with site, media, transaction, and communication failures. Site failure of a mobile unit is fre- 29.1 Mobile Databases I 921 quent due to limited battery power. A voluntary shutdown of a mobile unit should not be treated as a failure. Transaction failures are routine during handoff when a mobile unit crosses cells. The transaction manager should be able to deal with such frequent failures. • Mobile database design: The global name resolution problem for handling queries is compounded because of mobility and frequent shutdown. Mobile database design must consider many issues of metadata management-for example, the constant updating of location information. • Location-based service: As clients move, location-dependent cache information may become stale. Eviction techniques are important in this case. Furthermore, frequently updating location dependent queries, then applying these (spatial) queries in order to refresh the cache poses a problem. • Division of labor: Certain characteristics of the mobile environment force a change in the division of labor in query processing. In some cases, the client must function independent of the server. However, what are the consequences of allowing full independent access to replicated data? The relationship between client responsibilities and their consequences has yet to be developed. • Security: Mobile data is less secure than that which is left at the fixed location. Proper techniques for managing and authorizing access to critical data become more important in this environment. Data is also more volatile, and techniques must be able to compensate for its loss. 29.1.4 Application: Intermittently Synchronized Databases One mobile computing scenario is becoming increasingly commonplace as people con- duct their work away from their offices and homes and perform a wide range of activities and functions: all kinds of sales, particularly in pharmaceuticals, consumer goods, and industrial parts; law enforcement; insurance and financial consulting and planning; real estate or property management activities; courier and transportation services, and so on. In these applications, a server or a group of servers manages the central database and the clients carry laptops or palmtops with a resident DBMS software to do "local" transaction activity for most of the time. The clients connect via a network or a dial-up connection (or possibly even through the Internet) with the server, typically for a short session-say, 30 to 60 minutes. They send their updates to the server, and the server must in turn enter them in its central database, which must maintain up-to-date data and prepare appropriate copies for all clients on the system. Thus, whenever clients connect-through a process known in the industry as synchronization of a client with a server-they receive a batch of updates to be installed on their local database. The primary characteristic of this scenario is that the clients are mostly disconnected; the server is not necessarily able to reach them. This environment has problems similar to those in distributed and client- server databases, and some from mobile databases, but presents several additional research problems for investigation. We refer to this environment as Intermittently Synchronized 922 IChapter 29 Emerging Database Technologies and Applications Database Environment (ISOBE), and the corresponding databases as Intermittently Syn- chronized Databases (ISOBs). Together, the following characteristics of ISOB's make them distinct from the mobile databases we have discussed thus far: 1. A client connects to the server when it wants to exchange updates. This communication may be unicast-one-on-one communication between the server and the client-or multicast-one sender or server may periodically communicate to a set of receivers or update a group of clients. 2. A server cannot connect to a client at will. 3. Issues of wireless versus wired client connections and power conservation are generally immaterial. 4. A client is free to manage its own data and transactions while it is disconnected. It can also perform its own recovery to some extent. 5. A client has multiple ways of connecting to a server and, in case of many servers, may choose a particular server to connect to based on proximity, communication nodes available, resources available, etc. Because of such differences, there is a need to address a number of problems related to ISOBs that are different from those typically involving mobile database systems. These include server database design for server databases, consistency and synchronization management among client and server databases, transaction and update processing, efficient use of the server bandwidth, and achieving scalability in the ISOB environments. 29.1.5 Selected Bibliography for Mobile Databases There has been a sudden surge of interest in mobile computing, and research on mobile databases has had a significant growth for the last five to six years. The June 1995 issue of Byte magazine discusses many aspects of mobile computing. Among books written on this topic, Dhawan (1997) is an excellent source on mobile computing. Wireless networks and their future are discussed in Holtzman and Goodman (1993). Imielinski and Badrinath (1994) provide a good survey of mobile database issues and also discuss in Imielinski and Badrinath (1992) data and metadata allocation in a mobile architecture. Dunham and Helal (1995) discuss problems of query processing, data distribution, and transaction management for mobile databases. Foreman and Zahorjan (1994) describe the capabilities and the problems of mobile computing and make a convincing argument in its favor as a viable solution for many information system applications of the future. Pitoura and Samaras (1998) describe all aspects of mobile database problems and solutions. Chrysanthis (1993) describes a transaction model that is designed to operate in an environment with mobile clients. In particular, this model allows a client to share the transaction processing load with proxies in order to facilitate mobility. Bertino et al. (1998) discuss approaches to fault tolerance and recovery in mobile databases. Acharya et al. (1995) consider broadcast schedules that minimize average query latency, and explore the impact of such schedules on optimal client caching strategies. Milojicic et al. (2002) present a tutorial on peer-to-peer comput- 29.2 Multimedia Databases I 923 ing. Corson and Macker (1999) is a response to IETF(1999) report that discusses the mobile ad-hoc networking protocol performance issues. Broadcasting (or pushing) data as a means of scalably disseminating information to clients is covered in Yeeet al. (2002). Chintalapati et al. (1997) provide an adaptive location management algorithm. Jensen et al. (200l) discuss data management issues as they pertain to location-based services. Wolfson (200l) describes a novel way of efficiently modeling object mobility by describing position using trajectories instead of points. For an initial discussion of the ISOB scalability issues and an approach by aggregation of data and grouping of clients, see Mahajan et al. (1998). Specific aggregation algorithms for grouping data at the server in ISOB applications are described in Yeeet al. (200l). Gray et al. (1993) discuss ISOB update conflicts and resolution techniques under various ISOB architectures. Breibart et al. (1999) go into further detail about deferred synchronization algorithms for replicated data. 29.2 MULTIMEDIA DATABASES In the years ahead multimedia information systems are expected to dominate our daily lives. Our houses will be wired for bandwidth to handle interactive multimedia applications. Our high-definition TV jcomputer workstations will have access to a large number of databases, including digital libraries, image and video databases that will distribute vast amounts of multisource multimedia content. 29.2.1 The Nature of Multimedia Data and Applications In Section 24.3 we discussed the advanced modeling issues related to multimedia data. We also examined the processing of multiple types of data in Chapter 22 in the context of object relational OBMSs (OROBMSs). OBMSs have been constantly adding to the types of data they support. Today the following types of multimedia data are available in current systems: • Text: May be formatted or unformatted. For ease of parsing structured documents, standards like SOML and variations such as HTML are being used. • Graphics: Examples include drawings and illustrations that are encoded using some descriptive standards (e.g., COM, PICT, postscript}. • Images: Includes drawings, photographs, and so forth, encoded in standard formats such as bitmap, JPEO, and MPEO. Compression is built into JPEO and MPEO. These images are not subdivided into components. Hence querying them by content (e.g., find all images containing circles) is nontrivial. • Animations: Temporal sequences of image or graphic data. • Video: A set of temporally sequenced photographic data for presentation at specified rates-for example, 30 frames per second. • Structured audio: A sequence of audio components comprising note, tone, duration, and so forth. 924 I Chapter 29 Emerging Database Technologies and Applications • Audio: Sample data generated from aural recordings in a string of bits in digitized form. Analog recordings are typically converted into digital form before storage. • Composite or mixed multimedia data: A combination of multimedia data types such as audio and video which may be physically mixed to yield a new storage format or log- ically mixed while retaining original types and formats. Composite data also contains additional control information describing how the information should be rendered. Nature of Multimedia Applications. Multimedia data may be stored, delivered, and utilized in many different ways. Applications may be categorized based on their data management characteristics as follows: • Repository applications: A large amount of multimedia data as well as metadata is stored for retrieval purposes. A central repository containing multimedia data may be maintained by a DBMS and may be organized into a hierarchy of storage levels-local disks, tertiary disks and tapes, optical disks, and so on. Examples include repositories of satellite images, engineering drawings and designs, space photographs, and radiol- ogy scanned pictures. • Presentation applications: A large number of applications involve delivery of multimedia data subject to temporal constraints. Audio and video data are delivered this way; in these applications optimal viewing or listening conditions require the DBMS to deliver data at certain rates offering "quality of service" above a certain threshold. Data is con- sumed as it is delivered, unlike in repository applications, where it may be processed later (e.g., multimedia electronic mail). Simple multimedia viewing of video data, for example, requires a system to simulate VCR-like functionality. Complex and interactive multimedia presentations involve orchestration directions to control the retrieval order of components in a series or in parallel. Interactive environments must support capabilities such as real-time editing analysis or annotating of video and audio data. • Collaborative work using multimedia information: This is a new category of applications in which engineers may execute a complex design task by merging drawings, fitting subjects to design constraints, and generating new documentation, change notifica- tions, and so forth. Intelligent healthcare networks as well as telemedicine will involve doctors collaborating among themselves, analyzing multimedia patient data and information in real time as it is generated. All of these application areas present major challenges for the design of multimedia database systems. 29.2.2 Data Management Issues Multimedia applications dealing with thousands of images, documents, audio and video seg- ments, and free text data depend critically on appropriate modeling of the structure and content of data and then designing appropriate database schemas for storing and retrieving multimedia information. Multimedia information systems are very complex and embrace a large set of issues, including the following: 29.2 Multimedia Databases I925 • Modeling: This area has the potential for applying database versus information retrieval techniques to the problem. There are problems of dealing with complex objects (see Chapter 20) made up of a wide range of types of data: numeric, text, graphic (com- puter-generated image), animated graphic image, audio stream, and video sequence. Documents constitute a specialized area and deserve special consideration. • Design: The conceptual, logical, and physical design of multimedia databases has not been addressed fully, and it remains an area of active research. The design process can be based on the general methodology described in Chapter 12, but the performance and tuning issues at each level are far more complex. • Storage: Storage of multimedia data on standard disklike devices presents problems of representation, compression, mapping to device hierarchies, archiving, and buffering during the input/output operation. Adhering to standards such as JPEO or MPEO is one way most vendors of multimedia products are likely to deal with this issue. In DBMSs, a "BLOB" (Binary Large Object) facility allows untyped bitmaps to be stored and retrieved. Standardized software will be required to deal with synchronization and compression/decompression, and will be coupled with indexing problems, which are still in the research domain. • Queries and retrieval: The "database" way of retrieving information is based on query languages and internal index structures. The "information retrieval" way relies strictly on keywords or predefined index terms. For images, video data, and audio data, this opens up many issues, among them efficient query formulation, query execution, and optimization. The standard optimization techniques we discussed in Chapter 16 need to be modified to work with multimedia data types. • Performance: For multimedia applications involving only documents and text, performance constraints are subjectively determined by the user. For applications involving video playback or audio-video synchronization, physical limitations dominate. For instance, video must be delivered at a steady rate of 60 frames per second. Techniques for query optimization may compute expected response time before evaluating the query. The use of parallel processing of data may alleviate some problems, but such efforts are currently subject to further experimentation. Such issues have given rise to a variety of open research problems. We look at a few representative problems now. 29.2.3 Open Research Problems Information Retrieval Perspective in Querying Mutimedia Databases. Modeling data content has not been an issue in database models and systems because the data has a rigid structure and the meaning of a data instance can be inferred from the schema. In contrast, information retrieval (IR) is mainly concerned with modeling the content of text documents (through the use of keywords, phrasal indexes, semantic networks, word frequencies, soundex encoding, and so on) for which structure is generally neglected. By modeling content, the system can determine whether a document is relevant to a query 926 I Chapter 29 Emerging Database Technologies and Applications by examining the content-descriptors of the document. Consider, for instance, an insurance company's accident claim report as a multimedia object: it includes images of the accident, structured insurance forms, audio recordings of the parties involved in the accident, the text report of the insurance company's representative, and other information. Which data model should be used to represent multimedia information such as this? How should queries be for- mulated against this data? Efficient execution thus becomes a complex issue, and the semantic heterogeneity and representational complexity of multimedia information gives rise to many new problems. Requirements of Multimedia/Hypermedia Data Modeling and Retrieval. To capture the full expressive power of multimedia data modeling, the system should have a general construct that lets the user specify links between any two arbitrary nodes. Hyperme- dia links, or hyperlinks, have a number of different characteristics: • Links can be specified with or without associated information, and they may have large descriptions associated with them. • Links can start from a specific point within a node or from the whole node. • Links can be directional or nondirectional when they can be traversed in either direction. The link capability of the data model should take into account all of these variations. When content-based retrieval of multimedia data is needed, the query mechanism should have access to the links and the link-associated information. The system should provide facilities for defining views over all links-private and public. Valuable contextual information can be obtained from the structural information. Automatically generated hypermedia links do not reveal anything new about the two nodes, and in contrast to manually generated hypermedia links, would have different significance. Facilities for creating and utilizing such links, as well as developing and using navigational query languages to utilize the links, are important features of any system permitting effective use of multimedia information. This area is important to interlinked databases on the www. The World Wide Web presents an opportunity to access a vast amount of information via an array of unstructured and structured databases that are interlinked. The phenomenal success and growth of the web has made the problem of finding, accessing, and maintaining this information extremely challenging. For the last few years several projects are attempting to define frameworks and languages that will allow us to define the semantic content of the web that will be machine processable. The effort is collectively known by the term semantic web. The RDF (resource description framework), XHTML (Extensible Hypertext Markup Language), DAML (DARPA Agent Markup Language), and OIL (Ontology Inference Layer) are among some of its major components.t Further details are outside the scope of our discussion. Indexing of Images. There are two approaches to indexing images: (1) identifying objects automatically through image-processing techniques, and (2) assigning index terms 4. See Fensel (2000) for an overview of these terms. 29.2 Multimedia Databases I 927 and phrases through manual indexing. An important problem in using image-processing techniques to index pictures relates to scalability. The current state of the art allows the indexing of only simple patterns in images. Complexity increases with the number of recog- nizable features. Another important problem relates to the complexity of the query. Rules and inference mechanisms can be used to derive higher-level facts from simple features of images. Similarly, abstraction can be used to capture concepts that are not simply possible to define in terms of a set of <attribute, value> pairs. This allows high-level queries like "find hotel buildings that have open foyers and allow maximum sunshine in the front desk area" in an architectural application. The information-retrieval approach to image indexing is based on one of three indexing schemes: 1. Classificatory systems: Classifies images hierarchically into predetermined categories. In this approach, the indexer and the user should have a good knowledge of the available categories. Finer details of a complex image and relationships among objects in an image cannot be captured. 2. Keyword-based systems: Uses an indexing vocabulary similar to that used in the indexing of textual documents. Simple facts represented in the image (like "ice- capped region") and facts derived as a result of high-level interpretation by humans (like permanent ice, recent snowfall, and polar ice) can be captured. 3. Entity-attribute-relationship systems: All objects in the picture and the relationships between objects and the attributes of the objects are identified. In the case of text documents, an indexer can choose the keywords from the pool of words available in the document to be indexed. This is not possible in the case of visual and video data. Problems in Text Retrieval. Text retrieval has always been the key feature in business applications and library systems, and although much work has gone into some of the following problems, there remains an ongoing need for improvement, especially regarding the following issues: • Phrase indexing: Substantial improvements can be realized if phrase descriptors (as opposed to single-word index terms) are assigned to documents and used in queries, provided that these descriptors are good indicators of document content and information need. • Use of thesaurus: One reason for the poor recall of current systems is that the vocabulary of the user differs from the vocabulary used to index the documents. One solution is to use a thesaurus to expand the user's query with related terms. The problem then becomes one of finding a thesaurus for the domain of interest. Another resource in this context is ontologies. An ontology necessarily entails or embodies some sort of world view with respect to a given domain. The world view is often conceived as a set of concepts (e.g. entities, attributes, process), their definitions and their inter- relationships which describe a target world. An ontology can be constructed in two ways, domain dependent and generic. The purpose of generic ontologies is to make a 928 IChapter 29 Emerging Database Technologies and Applications general framework for all ( or most) categories encountered by human existence. A variety of domain ontologies such as gene ontology (see Section 29.4) or ontology for electronic components have been constructed' • Resolving ambiguity; One of the reasons for low precision (the ratio of the number of relevant items retrieved to the total number of retrieved items) in text information retrieval systems is that words have multiple meanings. One way to resolve ambiguity is to use an online dictionary or ontology; another is to compare the contexts in which the two words occur. In the first three decades of DBMS development-roughly from 1965 to 1995-the primary focus had been on the management of mostly numeric business and industrial data. In the next few decades, nonnumeric textual information will probably dominate database content. The text retrieval problem is becoming very relevant in the context of HTML and XML documents. The web currently contains several billion of these pages. Search engines find relevant documents given lists of words which is a case of free form natural language query. Obtaining the corrrect result that meets the requirements of both precision (% of retrieved documents that are relevant) and recall (% of total relevant documents that are retrieved), which are standard metrics in information retrieval, remains a challenge. As a consequence, a variety of functionalities involving comparison, conceptualization, under- standing, indexing, and summarization of documents will be added to DBMSs. Multimedia information systemspromise to bring about a joining of disciplines that have historically been separate areas: information retrieval and database management. 29.2.4 Multimedia Database Applications Large-scale applications of multimedia databases can be expected to encompass a large number of disciplines and enhance existing capabilities. Some important applications will be involved: • Documentsand records management: A large number of industries and businesses keep very detailed records and a variety of documents. The data may include engineering design and manufacturing data, medical records of patients, publishing material, and insurance claim records. • Knowledge dissemination: The multimedia mode, a very effective means of knowledge dissemination, will encompass a phenomenal growth in electronic books, catalogs, manuals, encyclopedias and repositories of information on many topics. • Education and training: Teaching materials for different audiences-from kindergarten students to equipment operators to professionals-can be designed from multimedia sources. Digital libraries are expected to have a major influence on the way future students and researchers as well as other users will access vast repositories of educa- tional material. 5. A good discussion of ontologies is given in Uschold and Gruninger (1996). [...]... Swiss-prot + TrEMBL) and summarize important issues of database management of such resources They discuss three main types of databases: Sequence Databases such as DDBJJEMBL/ GENEBANK Nucleotide Sequence Database; Secondary Databases such as PROSITE, PRINTS and Pfam; and Integrated Databases such as InterPro, that integrates data from six major protein signature databases (Pfam, PRINTS, ProDom, PROSITE, SMART,... in biological databases change at a rapid pace Hence, for improved information flow between generations or releases of databases, schema evolution and data object migration must be supported The ability to extend the schema, a frequent occurrence in the biological setting, is unsupported in most relational and object database systems Presently systems such as GenBank rerelease the entire database with... actual data structures is supported In addition, no mechanism for evolving the schema is documented Table 29.1 summarizes the features of the major genome-related databases, as well as HGMOB and ACEOB databases Some additional protein databases exist; they contain information about protein structures Prominent protein databases include SWISSPROT at the University of Geneva, Protein Data Bank (POB) at Brookhaven... multimedia databases will expand the range of these applications • Real-time control and monitoring: Coupled with active database technology (see Chapter 24), multimedia presentation of information can be a very effective means for monitoring and controlling complex tasks such as manufacturing operations, nuclear power plants, patients in intensive care units, and transportation systems Commercial Systems. .. Structure Database (EMSD), which is a relational database (http://www.ebi.ac.uk/msd) (Boutselakis et al., 2003) is designed to be a single access point for protein and nucleic acid structures and related information The database is derived from Protein Data Bank (PDB) entries The search database contains an extensive set of derived properties, goodness-of-fit indicators, and links to other EBI databases... Emerging Database Technologies and Applications during biological data modeling The structure of biological data often provides an additional context for interpretation of the information Biological information systems must be able to represent any level of complexity in any data schema, relationship, or schema substructure-not just hierarchical, binary, or table data As an example, MITOMAP is a database documenting... read-access patterns into the database, but these patterns are not the same as those seen in traditional relational databases User requested ad hoc searches demand indexing of often unexpected combinations of data instance classes Characteristic 6: Most biologists are not likely to have any knowledge of the internal structure of the database or about schema design Biological database interfaces should... are about to be updated in a biological database cannot simply be thrown away All of these characteristics clearly point to the fact that today's DBMSs do not fully cater to the requirements of complex biological data A new direction in database management systems is necessary," 9 See Kogelnik et al (1997, 1998) for further details I 939 940 I Chapter 29 Emerging Database Technologies and Applications... variety of databases mentioned in this section He defines two types of 29.4 Genome Data Management links: those that integrate the data and those that relate the data between databases These were used to design the Ecocyc database Some of the important web links include the following: The Human Genome sequence information can be found at: http://www.ncbLnlm.nih.gov/genome/seq/ The MITOMAP database developed... biggest protein database SWISS-PROT can be accessed from http://expasy.hcuge.ch/sprot/ The ACEDB database information is available at http://probe.nalusda.gov:8080/acedocs/ I 945 Alternative Diagrammatic Notations for ER Models Figure A.I shows a number of different diagrammatic notations for representing ER and EER model concepts Unfortunately, there is no standard notation: different database design . typically involving mobile database systems. These include server database design for server databases, consistency and synchronization management among client and server databases, transaction and. this environment as Intermittently Synchronized 922 IChapter 29 Emerging Database Technologies and Applications Database Environment (ISOBE), and the corresponding databases as Intermittently Syn- chronized Databases (ISOBs). Together, the following. in the biological setting, is unsupported in most relational and object database systems. Presently systems such as GenBank rerelease the entire database with new schemas once or twice a year rather than incrementally

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