Raghav Srinivasan, Chao Liang, Krithi Ramamritham, "Maintaining Temporal Coherency of Virtual Data Warehouses", Proc. of 19th IEEE Symposium on Real-Time Systems (RTSS'98), Madrid (December 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....are small, and objects change infrequently. Adaptive TTL [6] Here proxies provide servers with feedback about an object s popularity and thus enable the server to compute suitable TTL values for objects rather than assign fixed or arbitrary TTL values. Time to refresh (TTR) techniques [39, 41]: These investigate proxy based techniques for maintaining both individual and mutual consistency for cached web objects. The idea here is to provide user desired fidelity or consistency guarantees by making proxies track the rate of change of objects they cache, at the server and thus ....

R. Srinivasan, C. Liang, and K. Ramamritham. Maintaining temporal coherency of virtual data warehouses, 1998.

....from the air while it is being broadcast. Since many data items in a mobile computing system are used to record the real time information in the system, e.g. the current traffic conditions of the roads, the last traded prices of stocks, and news updates, their values could be highly dynamic [24]. Updates, which are generated from external devices, capture the most current information for the system and refresh the values of the data items [25, 26] Allowing execution of updates to be interleaved with data broadcast is important in maintaining the freshness of the data items [25] ....

Srinivasan, R., Liang C. and Ramamritham, K., "Maintaining Temporal Coherency of Virtual Data Warehouses", in Proceedings of Real-time Systems Symposium, Spain, 1998.

....data items in a mobile network can be very expensive, an alternative is to bound the degree of staleness of the data items within a pre defined bound. For some data items, e.g. stock information, the data values will be totally useless if they are older than a certain pre defined time bound [19]. Note that this requirement has been generally ignored in most of the previous studies [18, 16, 17, 20] although it is important due to the realtime properties of the data items [19, 23] 4.2 Data Inconsistency Problems In this section, we briefly describe the inconsistency problem in data ....

.... information, the data values will be totally useless if they are older than a certain pre defined time bound [19] Note that this requirement has been generally ignored in most of the previous studies [18, 16, 17, 20] although it is important due to the realtime properties of the data items [19, 23]. 4.2 Data Inconsistency Problems In this section, we briefly describe the inconsistency problem in data broadcast using some representative examples. Example 1: Data conflict between a MT and an update transaction. Suppose the update transaction, U, will update data item d 5 and then data ....

Srinivasan, R., Liang C. and Ramamritham, K., "Maintaining Temporal Coherency of Virtual Data Warehouses", in Proceedings of Real-time Systems Symposium, Spain, 1998.

....bandwidth, it is not a good idea to send data request of a mobile transaction to the database server. In addition, the efficient methods for traditional database systems are not suitable. Secondly, the values of the data items must be up todate or at least close 2 to the most current values [22, 23, 24]. It addresses the need to maintain the temporal constraints of data since the values of the data items can be highly dynamic. These two issues unfortunately have received much less attention than the design of data broadcast algorithms even though they are also critical in the design of practical ....

Srinivasan, R., Liang C., Ramamritham, K. (1998) Maintaining Temporal Coherency of Virtual Data Warehouses. Proceeding of Real Time Systems Symposium, Madrid, Spain, 2-4 Dec, p.60-70.

....in mobile communication technology have greatly increased the functionality of mobile information services. An important application of mobile computing systems is to provide various types of real time information such as stock quotes, weather conditions and traffic information, to mobile clients [SLR99]. A number of efficient data dissemination strategies have been proposed in recent years. Amongst the proposed methods, most are based on data broadcast as it is very cost effective in disseminating a substantial amount of information to a large number of mobile clients [AAFZ95, DCKV97] In a ....

.... Invalidity Interval (AVI) An important characteristic of the data items in a mobile computing system is that they possess different degrees of real time properties, e.g. they often represent the current status of the objects in the external environment, whose value may change quite rapidly [SLR99]. Examples include news updates and the latest market prices of stocks. Due to the real time properties of the data items, updates, which are captured by some external devices or obtained from data vendors, are required to maintain the validity of the data values. Usually, stale (invalid) data ....

Srinivasan, R., Liang C., Ramamritham, K., "Maintaining Temporal Coherency of Virtual Data Warehouse", Proc. Real Time Systems Symposium, 1999.

....determined dynamically based on system status such as disconnection frequency and duration as well as update and query pattern. There is also a considerable amount of research in Web based cache coherence, but these studies are confined primarily to Web specific protocols and in wired networks [CL98, SLR99]. 3 The Absolute Invalidity Interval (AVI) of a Data Item One important characteristic of the data items in the database of a mobile computing system is that they often represent the current status of the objects in the external environment, whose value may change quite rapidly. Examples are ....

Srinivasan, R., Liang C., Ramamritham, K., "Maintaining Temporal Coherency of Virtual Data Warehouse", Proc. Real Time Systems Symposium, 1999.

....workload will be very heavy, and, as a result, the completion times of updates and the validity of many data items might be seriously affected. On the other hand, if the update rate is low, the uncertainty level of data validity might be high, and the external consistency cannot be maintained [1]. In addition to the unreliable mobile network, the mobility of clients complicates the design of a mobile information system. Queries from mobile clients could have some locationdependent properties. For example, the result of a query might depend on the current location of the originating ....

R. Srinivasan, C. Liang and K. Ramamritham, "Maintaining Temporal Coherency of Virtual Data Warehouses", in Proceedings of Realtime Systems Symposium, Spain, 1998.

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R. Srinivasan, C. Liang and K. Ramamritham, Maintaining Temporal Coherency of Virtual Data Warehouses, The 19th IEEE Real-Time Systems Symposium, Madrid, Spain, December 2-4, 1998.

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Raghav Srinivasan, Chao Liang and Krithi Ramamritham, Maintaining Temporal Coherency of Virtual Data Warehouses, The 19th IEEE Real-Time Systems Symposium, Madrid, Spain, December 2-4, 1998.

....Adaptive Polling: In this policy, a peer dynamically varies the polling frequency based on the update rate for the file frequently modified files are polled more frequently than relatively static files. The notion of adaptive polling has been explored in the context of web cache consistency [6, 7] and we use a similar idea here. A time to refresh (TTR) value is associated with each file. The TTR denotes the next time instant the peer must poll the owner, and thus, determines the polling frequency. The TTR value is varied dynamically based on the results of each poll message. The TTR value ....

R. Srinivasan, C. Liang, and K. Ramamritham, Maintaining Temporal Coherency of Virtual Data Warehouses, The 19th IEEE Real-Time Systems Symposium, Madrid, Spain, December 2-4, 1998

....are of interest to the user need to be pulled from the server (and the TTR is computed accordingly) Clearly, the success of the pull based technique hinges on the accurate estimation of the TTR value. Next, we summarize a set of techniques for computing the TTR value that have their origins in [22]. Given a user s coherency requirement, these techniques allow a proxy to adaptively vary the TTR value based on the rate of change of the data item. The TTR decreases dynamically when a data item starts changing rapidly and increases when a hot data item becomes cold. To achieve this objective, ....

....of the algorithm and can be adjusted dynamically depending on the temporal coherency desired, with a stringent coherency demanding a higher value of a. The adaptive TTR approach has been experimentally shown to have the best temporal coherency properties among several TTR assignment approaches [22]. Consequently, we choose this technique as the basis for pull based dissemination. Note that the Time To Refresh (TTR) value is different from the Time to Live (TTL) value associated with each HTTP request. The former is computed by a proxy to determine the next time it should poll the server ....

[Article contains additional citation context not shown here]

Raghav Srinivasan, Chao Liang and Krithi Ramamritham, Maintaining Temporal Coherency of Virtual Data Warehouses, The 19th IEEE Real-Time Systems Symposium, Madrid, Spain, December 2-4, 1998.

....are of interest to the user need to be pulled from the server (and the TTR is computed accordingly) Clearly, the success of the pull based technique hinges on the accurate estimation of the TTR value. Next, we summarize a set of techniques for computing the TTR value that have their origins in [22]. Given a user s coherency requirement, these techniques allow a proxy to adaptively vary the TTR value based on the rate 2 Note that the Time To Refresh (TTR) value is different from the Time to Live (TTL) value associated with each HTTP request. The former is computed by a proxy to determine ....

.... 0 a 1 is a parameter of the algorithm and can be adjusted dynamically depending on the fidelity desired, with a higher fidelity demanding a higher value of a. The adaptive TTR approach has been experimentally shown to have the best tc properties among several TTR assignment approaches [22]. Consequently, we choose this technique as the basis for pull based dissemination. 7 B. Push In a push based approach, the proxy registers with a server, identifying the data of interest and the associated tcr, i.e. the value c. Whenever the value of the data changes, the server uses the tcr ....

[Article contains additional citation context not shown here]

Raghav Srinivasan, Chao Liang and Krithi Ramamritham, Maintaining Temporal Coherency of Virtual Data Warehouses, The 19th IEEE Real-Time Systems Symposium (RTSS98), Madrid, Spain, December 2-4, 1998.

....a Time To Refresh (TTR) for the data item. denotes the next time that the proxy should poll the server so as to refresh the data item if it has changed in the interim. The success of the pull based technique hinges on the accurate estimation of the TTR value. We use the Adaptive TTR Algorithm[9], to calculate the TTR associated with each data item. This algorithm allows the proxy to adaptively vary the TTR value based on the rate of change of the data item. The TTR decreases dynamically when a data item starts changing rapidly and increases when changes are smaller and slower. To achieve ....

....To achieve this objective, the Adaptive TTR approach takes into account static bounds so that TTR values are not set too high or too low, the most rapid changes that have occurred so far and most recent changes to the polled data. This algorithm provides good fidelity and incurs low network [9] overheads and hence we use it in our algorithm. 3. CBA: The Balancing Algorithm The Balancing Algorithm (CBA) continuously changes the associated with each of the data items constituting the portfolio based on the dynamics of the data, as well as the relative contribution of the data item to ....

[Article contains additional citation context not shown here]

R. Srinivasan, C. Liang and K. Ramamritham, Maintaining Temporal Coherency of Virtual Data Warehouses, The 19th IEEE Real-Time Systems Symposium, Madrid, Spain, December 2-4, 1998.

No context found.

R. Srinivasan, C. Liang, and K. Ramamritham. Maintaining Temporal Coherency of Virtual Data Warehouses. In IEEE Real-Time Systems Symposium (RTSS98), Dec. 1998. 39

....are of interest to the user need to be pulled from the server (and the TTR is computed accordingly) Clearly, the success of the pull based technique hinges on the accurate estimation of the TTR value. Next, we summarize a set of techniques for computing the TTR value that have their origins in [21]. Given a user s coherency requirement, these techniques allow a proxy to adaptively vary the TTR value based on the rate of change of the data item. The TTR decreases dynamically when a data item starts changing rapidly and increases when a hot data item becomes cold. To achieve this objective, ....

....# is a parameter of the algorithm and can be adjusted dynamically depending on the fidelity desired, with a higher fidelity demanding a higher value of #. The adaptive TTR approach has been experimentally shown to have the best temporal coherency properties among several TTR assignment approaches [21]. Consequently, we choose this technique as the basis for pull based dissemination. 2.2 Push In a push based approach, the proxy registers with a server, identifying the data of interest and the associated ###, i.e. the value #. Whenever the value of the data changes, the server uses the ### ....

[Article contains additional citation context not shown here]

Raghav Srinivasan, Chao Liang and Krithi Ramamritham, Maintaining Temporal Coherency of Virtual Data Warehouses, The 19th IEEE Real-Time Systems Symposium, Madrid, Spain, December 2-4, 1998.

No context found.

Raghav Srinivasan, Chao Liang, Krithi Ramamritham, "Maintaining Temporal Coherency of Virtual Data Warehouses", Proc. of 19th IEEE Symposium on Real-Time Systems (RTSS'98), Madrid (December 1998.

No context found.

Raghav Srinivasan, Chao Liang, Krithi Ramamritham, "Maintaining Temporal Coherency of Virtual Data Warehouses", Proc. of 19th IEEE Symposium on Real-Time Systems (RTSS'98), Madrid (December 1998.

No context found.

R. Sirnivasan, C. Liang, and Krithi Ramamritham, Maintaining Temporal Coherency of Virtual Data Warehouses, The 19th IEEE Real-Time Systems Symposium, Madrid, Spain, December 2-4, 1998

No context found.

R. Srinivasan, C. Liang, and Krithi Ramamritham, Maintaining Temporal Coherency of Virtual Data Warehouses, The 19th IEEE Real-Time Systems Symposium, Madrid, Spain, December 2-4, 1998

No context found.

Srinivasan, R., Liang C., Ramamritham, K., "Maintaining Temporal Coherency of Virtual Data Warehouse", in Proceeding of Real Time Systems Symposium, 1999.

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