New Applications in Enterprise Database Systems Technological Educational Institution of Larissa in collaboration with Staffordshire University Larissa 2006 Dr. Georgia Garani garani@teilar. gr Dr. Theodoros Mitakos teo_ms@yahoo. com
Multimedia Databases 1/2 DBMSs have been constantly adding to the types of data they support. Τoday the following types of multimedia data are available ίη current systems: Text: May be formatted or unformatted. For ease of parsing structured documents, standards like SGML and variations such as ΗΤΜΙ are being used. Graphics: Examples include drawings and illustrations that are encoded using some descriptive standards (e. g. , CGM, PICT, postscript). lmages: Includes drawings, photographs, and so forth, encoded ίη standard formats such as bitmap, JPEG, and MPEG. Compression is built into JPEG and MPEG. Τhese images are not subdivided into components. Hence querying them by content (e. g. , find all images containing circles) is nontrivial. Animations: Τemporal sequences of image οτ graphic data. Video: Α set of temporally sequenced photographic data for presentation at specified rates for example, 30 frames per second. Structured audio: Α sequence of audio components comprising note, tone, duration, and so forth. Audio: Sample data generated from aural recordings ίη a string of bits ίη digitized form. Analog recordings are typically converted into digital form before storage. Composite or mixed multimediα dαtα: Α combination of multimedia data types such as audio and video which may be physically mixed to yield a new storage format οr log ically mixed while retaining original types and formats. Composite data also contains additional control information describing how the information should be rendered.
Multimedia Databases 2/2 Nature of Multimedia Applications. Multimedia data may be stored, delivered, and utilized ίη many different ways. Applications may be categorized based οη their data management characteristics as follows: Repository αpplicαtions: Α large amount of multimedia data as well as metadata is stored for retrieval purposes. Α 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 οη. Examples include repositories of satellite images, engineering drawings and designs, space photographs, and radiol ogy scanned pictures. Presentαtion αpplicαtions: Α large number of applications involve delivery of multimedia data subject to temporal constraints. Audio and video data are delivered this wayj ίη these applications optimal viewing οτ listening conditions require the DBMS to deliver data at certain rates offering "quality of serνice" above a certain threshold. Data is con sumed as it is delivered, unlikeίη 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 interac tive multimedia presentations involve orchestration directions to control the retrieval order of components ίη a series οτ ίη parallel. Interactive environments must support capabilities such as real time editing analysis οτ annotating of video and audio data. Collαborαtive work using multimediα inforrnαtion: This is a new category of applications ίη which engineers may execute a complex design task by merging drawings, fιtting subjects to design constraints, and generating new documentation, change notifιca tions and so forth. Intelligent , healthcare networks as well as telemedicine will involve doctors collaborating among themselves, analyzing multimedia patient data and information ίη real time as it is generated. ΑΙΙ of these application areas present major challenges for the design of multimedia database systems.
Data Management Requirements of GIS 1/4 Data Mode/ing and Representation. GIS data can be broadly represented ίη two formats: (1) vector and (2) raster. Vector data represents geometric abjects such as points, lines, and polygons. Thus a lake may be represented as a polygon, a river by a series σΕ line ; egments. Raster data is characterized as an aπaΥ σΕ points, where each point represents the ralue σΕ an attribute for a real world location. Informally, raster images are n dimensional lnays where each entry is a unit σΕ the image and represents an attribute. Τ wo dimensional Inits are called pixels, whίle three dimensional units are called voxels. Three dimensional : levation data is stored ίη a raster based digίtal elevation model (DEM) foπnat. Another ras er foπnat called triangular irregular network (ΤΙΝ) is a topological vector based approach hat models surfaces by connecting sample points as vertices σΕ triangles and has a point lensity that may vary with the roughness σΕ the teπaίη. Rectangular grids (or elevation matrices) are two dimensional auay structures. Ιη digital terrain modeling (DTM), the model also may be used by substituting the eleνation with some attribute of interest such as population density or air temperature. GIS data often includes a temporal structure ίη addi tionto a spatial structure. For example, traffic flow or aνerage νehicular speeds ίη traffic may be measured eνery 60 seconds at a set of points ίη a roadway nework.
Data Management Requirements of GIS 2/4 Data Analysis. GIS data undergoes νarious types of analysis. For example, ίη applica tionssuch as soil erosion studies, enνironmental impact studies, or hydrological runoff simu lations ΟΤΜ data may , undergo νarious types of geomorphometric analysis measurements such as slope νalues, graάients (the rate of change ίη altitude), αspect (the compass direction of the gradient), profile conve. XΊty (the rate of change of gradient), ριaη conve. XΊty (the con νexityof contours and other parameters). When GIS data is used for decision support appli cations it may undergo aggregation and expansion , operations using data warehousing, as we discussed ίη Section 28. 3. Ιη addition, geometric operations (to compute distances, areas, νolumes), topological operations (to compute oνerlaps, intersections, shortest paths), and temporal operations (to compute intemal based or eνent based queries) are inνolved. Analysis inνolνes a number of temporal and spatial operations
Data Management Requirements of GIS 3/4 Data Integration. GISs must integrate both νector and raster data from a variety of sources. Sometimes edges and regions are infeued from a raster image to form a νector model, or conνersely, raster images such as aerial photographs are used to update νector models. Sev eralcoordinate systems such as Uniνersal Τransνerse Mercator (UTM), latitude/longitude, and local cadastral systems are used to identify locations. Data originating from different coordi natesystems requires appropriate transformations. Major public sources of geographic data, including the TIGER fΪles maintained by U. S. Department of Commerce, are used for road maps by many Web based map drawing tools (e. g. , http: //maps. yahoo. com). αteη there are high accuracy, attribute poor maps that haνe to be merged with low accuracy, attrίbute ήch maps. This is done with a process called "rubber banding" where the user defΪnes a set of con trolpoints ίη both maps and the transformation of the low accuracy map is accomplished by lining υρ the control points. Α major integration issue is to create and maintain attribute information (such as air quality or traffic flow), which can be related to and integrated with appropriate geographical information oνer time as both eνolνe.
Data Management Requirements of GIS 4/4 Data Capture. The first step ίη deνeloping a spatial database for cartographic model ingis to capture the two dimensional or three dimensional geographical information ίη dig italform a process that is sometimes impeded by source map characteristics such as resolution, type of projection, map scales, cartographic licensing, diνersity of measurement techniques, and coordinate system differences. Spatial data can also be captured from remote sensors ίη satellites such as Landsat, NORA, and Adνanced Yery High Resolution Radiometer (AVHRR) as well as SPOT HRV (High Resolution Yisible Range lnstrument), . which is free of interpretiνe bias and νery accurate. For digital teuain modeling, data cap ture methods range from manual to fully automated. Ground surνeys are the traditional approach and the most accurate, but they are νery time consuming. Other techniques include photogrammetric sampling and digitizing cartographic documents.
Mobile databases 1/3 From a data management standpoint, mobile computing may be considered a νariation of distributed computing. Mabile databases can be distributed under two possible scenarios: The entire database is distributed mainly among the wired components, possibly with full or partial replication. Α base station or fixed host manages its own data· base with a DBMS like functionality, with additional functionality for locating mobile units and additional query and transaction management features to meet the requirements σΕ mobile enνironments. The database is distributed among wired and wireless components. Data manage· ment responsibility is shared among base stations or fixed hosts and mobile units.
Mobile databases 2/3 Oαtα distribution and replication: Data is uneνenly distributed among the base stations and mobile units. The consistency constraints compound the problem σΕ cache man· agement. Caches attempt to proνide the most frequently accessed and updated data to mobile units that process their own transactions and may be disconnected oνer long periods. Transαction models: Issues σΕ fault tolerance and correctness σΕ transactions are aggra· νated ίη the mobile enνironment. Α mobile transaction is executed sequentially through seνeral base stations and possibly ση multiple data sets depending upon the moνement σΕ the mobile unit. Central coordination σΕ transaction execution is lack· ing, particularly ίη scenario (2) aboνe. Moreoνer, a mobile transaction is expected to be long liνed because σΕ disconnection ίη mobile units. Hence, traditional ACID properties σΕ transactions (see Chapter 19) may need to be modified and new transac· tion models must be defined. Query processing: Awareness σΕ where data is located is important and affects the cost{ benefit analysis σΕ query processing. Query optimization is more complicated because σΕ mobility and rapid resource changes σΕ mobile units. The query response needs to be retumed to mobile units that may be ίη transit or may cross cell boundaries yet must receiνe complete and correct query results. Recovery αΜ fault tolerance: The mobile database enνironment must deal with site, media, transaction, and communication failures. Site failure σΕ a mobile unit is frequent due to limited battery power. Α voluntary shutdown of a mobile unit should not be treated as a failure. Τransaction failures are routine during handoff when a mobile unit crosses cells. Τhe transaction manager should be able to deal with such frequent failures.
Mobile databases 3/3 Mobile dαtαbαse design: Τhe 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. Locαtion-bαsed service: As clients move, location dependent cache information may become stale. Eviction techniques are important ίη this case. Furthermore, fre quentlyupdating location dependent queries, then applying these (spatial) queries ίη order to refresh the cache poses a problem. Division of labor: Certain characteristics of the mobile environment force a change ίη the division of labor ίη query processing. Ιη some cases, the client must function independent of the server. However, what are the consequences of allowing full inde pendentaccess 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 impor tantίη this environment. Data is also more volatile, and techniques must be able to compensate for its loss.
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