amol katkar - Academia.edu (original) (raw)
Papers by amol katkar
Sigmod Record, 2001
An important issue in the dissemination of timevarying web data such as sports scores and stock p... more An important issue in the dissemination of timevarying web data such as sports scores and stock prices is the maintenance of temporal coherency. In the case of servers adhering to the HTTP protocol, clients need to frequently pull the data based on the dynamics of the data and a user's coherency requirements. In contrast, servers that possess push capability maintain state information pertaining to clients and push only those changes that are of interest to a user. These two canonical techniques have complementary properties with respect to the level of temporal coherency maintained, communication overheads, state space overheads, and loss of coherency due to (server) failures. In this demonstration, we show how to combine push-and pull-based techniques to achieve the best features of both approaches. Our combined technique tailors the dissemination of data from servers to clients based on (i) the capabilities and load at servers and proxies, and (ii) clients' coherency requirements. By using our continual query system, we will show how diverse requirements of temporal coherency, resiliency and scalability can be met using our techniques.
Sigmod Record, 2001
An important issue in the dissemination of timevarying web data such as sports scores and stock p... more An important issue in the dissemination of timevarying web data such as sports scores and stock prices is the maintenance of temporal coherency. In the case of servers adhering to the HTTP protocol, clients need to frequently pull the data based on the dynamics of the data and a user's coherency requirements. In contrast, servers that possess push capability maintain state information pertaining to clients and push only those changes that are of interest to a user. These two canonical techniques have complementary properties with respect to the level of temporal coherency maintained, communication overheads, state space overheads, and loss of coherency due to (server) failures. In this demonstration, we show how to combine push-and pull-based techniques to achieve the best features of both approaches. Our combined technique tailors the dissemination of data from servers to clients based on (i) the capabilities and load at servers and proxies, and (ii) clients' coherency requirements. By using our continual query system, we will show how diverse requirements of temporal coherency, resiliency and scalability can be met using our techniques.
IEEE Transactions on Computers, 2002
AbstractÐAn important issue in the dissemination of time-varying web data such as sports scores a... more AbstractÐAn important issue in the dissemination of time-varying web data such as sports scores and stock prices is the maintenance of temporal coherency. In the case of servers adhering to the HTTP protocol, clients need to frequently pull the data based on the dynamics of the data and a user's coherency requirements. In contrast, servers that possess push capability maintain state information pertaining to clients and push only those changes that are of interest to a user. These two canonical techniques have complementary properties with respect to the level of temporal coherency maintained, communication overheads, state space overheads, and loss of coherency due to (server) failures. In this paper, we show how to combine push and pull-based techniques to achieve the best features of both approaches. Our combined technique tailors the dissemination of data from servers to clients based on 1) the capabilities and load at servers and proxies and 2) clients' coherency requirements. Our experimental results demonstrate that such adaptive data dissemination is essential to meet diverse temporal coherency requirements, to be resilient to failures, and for the efficient and scalable utilization of server and network resources.
Dynamic data is data which varies rapidly and unpredictably. This kind of data is generally used ... more Dynamic data is data which varies rapidly and unpredictably. This kind of data is generally used in on-line decision making and hence needs to be delivered to its users conforming to certain time or value based application-specific requirements. The main issue in the dissemination of dynamic web data such as stock prices, sports scores or weather data is the maintenance of temporal coherency within the user specified bounds. Since most of the web servers adhere to the HTTP protocol, clients need to frequently pull the data depending on the changes in the data and user’s coherency requirements. In contrast, servers that possess push capability maintain state information pertaining to user’s requirements and push only those changes that are of interest to a user. These two canonical techniques have complementary properties. In pure pull approach, the level of temporal coherency maintained is low while in pure push approach it is very high, but this is at the cost of high state space at the server which results in a less resilient and less scalable system. Communication overheads in pull-based schemes are high as compared to push-based schemes, since the number of messages exchanged in the pull approach are higher than in push based approach. Based on these observations, this paper explores different approaches to combining the two approaches so as to harness the benefits of both approaches.
An important issue in the dissemination of time-varying web data such as sports scores and stock ... more An important issue in the dissemination of time-varying web data such as sports scores and stock prices is the maintenance of temporal coherency. In the case of servers adhering to the HTTP protocol, clients need to frequently pull the data based on the dynamics of the data and a user's coherency requirements. In contrast, servers that possess push capability maintain state information pertaining to clients and push only those changes that are of interest to a user. These two canonical techniques have complementary properties with respect to the level of temporal coherency maintained, communication overheads, state space overheads, and loss of coherency due to (server) failures. In this paper, we show how to combine push-and pull-based techniques to achieve the best features of both approaches. Our combined technique tailors the dissemination of data from servers to clients based on (i) the capabilities and load at servers and proxies, and (ii) clients' coherency requirements. Our experimental results demonstrate that such adaptive data dissemination is essential to meet diverse temporal coherency requirements, to be resilient to failures, and for the efficient and scalable utilization of server and network resources.
Sigmod Record, 2001
An important issue in the dissemination of timevarying web data such as sports scores and stock p... more An important issue in the dissemination of timevarying web data such as sports scores and stock prices is the maintenance of temporal coherency. In the case of servers adhering to the HTTP protocol, clients need to frequently pull the data based on the dynamics of the data and a user's coherency requirements. In contrast, servers that possess push capability maintain state information pertaining to clients and push only those changes that are of interest to a user. These two canonical techniques have complementary properties with respect to the level of temporal coherency maintained, communication overheads, state space overheads, and loss of coherency due to (server) failures. In this demonstration, we show how to combine push-and pull-based techniques to achieve the best features of both approaches. Our combined technique tailors the dissemination of data from servers to clients based on (i) the capabilities and load at servers and proxies, and (ii) clients' coherency requirements. By using our continual query system, we will show how diverse requirements of temporal coherency, resiliency and scalability can be met using our techniques.
Sigmod Record, 2001
An important issue in the dissemination of timevarying web data such as sports scores and stock p... more An important issue in the dissemination of timevarying web data such as sports scores and stock prices is the maintenance of temporal coherency. In the case of servers adhering to the HTTP protocol, clients need to frequently pull the data based on the dynamics of the data and a user's coherency requirements. In contrast, servers that possess push capability maintain state information pertaining to clients and push only those changes that are of interest to a user. These two canonical techniques have complementary properties with respect to the level of temporal coherency maintained, communication overheads, state space overheads, and loss of coherency due to (server) failures. In this demonstration, we show how to combine push-and pull-based techniques to achieve the best features of both approaches. Our combined technique tailors the dissemination of data from servers to clients based on (i) the capabilities and load at servers and proxies, and (ii) clients' coherency requirements. By using our continual query system, we will show how diverse requirements of temporal coherency, resiliency and scalability can be met using our techniques.
IEEE Transactions on Computers, 2002
AbstractÐAn important issue in the dissemination of time-varying web data such as sports scores a... more AbstractÐAn important issue in the dissemination of time-varying web data such as sports scores and stock prices is the maintenance of temporal coherency. In the case of servers adhering to the HTTP protocol, clients need to frequently pull the data based on the dynamics of the data and a user's coherency requirements. In contrast, servers that possess push capability maintain state information pertaining to clients and push only those changes that are of interest to a user. These two canonical techniques have complementary properties with respect to the level of temporal coherency maintained, communication overheads, state space overheads, and loss of coherency due to (server) failures. In this paper, we show how to combine push and pull-based techniques to achieve the best features of both approaches. Our combined technique tailors the dissemination of data from servers to clients based on 1) the capabilities and load at servers and proxies and 2) clients' coherency requirements. Our experimental results demonstrate that such adaptive data dissemination is essential to meet diverse temporal coherency requirements, to be resilient to failures, and for the efficient and scalable utilization of server and network resources.
Dynamic data is data which varies rapidly and unpredictably. This kind of data is generally used ... more Dynamic data is data which varies rapidly and unpredictably. This kind of data is generally used in on-line decision making and hence needs to be delivered to its users conforming to certain time or value based application-specific requirements. The main issue in the dissemination of dynamic web data such as stock prices, sports scores or weather data is the maintenance of temporal coherency within the user specified bounds. Since most of the web servers adhere to the HTTP protocol, clients need to frequently pull the data depending on the changes in the data and user’s coherency requirements. In contrast, servers that possess push capability maintain state information pertaining to user’s requirements and push only those changes that are of interest to a user. These two canonical techniques have complementary properties. In pure pull approach, the level of temporal coherency maintained is low while in pure push approach it is very high, but this is at the cost of high state space at the server which results in a less resilient and less scalable system. Communication overheads in pull-based schemes are high as compared to push-based schemes, since the number of messages exchanged in the pull approach are higher than in push based approach. Based on these observations, this paper explores different approaches to combining the two approaches so as to harness the benefits of both approaches.
An important issue in the dissemination of time-varying web data such as sports scores and stock ... more An important issue in the dissemination of time-varying web data such as sports scores and stock prices is the maintenance of temporal coherency. In the case of servers adhering to the HTTP protocol, clients need to frequently pull the data based on the dynamics of the data and a user's coherency requirements. In contrast, servers that possess push capability maintain state information pertaining to clients and push only those changes that are of interest to a user. These two canonical techniques have complementary properties with respect to the level of temporal coherency maintained, communication overheads, state space overheads, and loss of coherency due to (server) failures. In this paper, we show how to combine push-and pull-based techniques to achieve the best features of both approaches. Our combined technique tailors the dissemination of data from servers to clients based on (i) the capabilities and load at servers and proxies, and (ii) clients' coherency requirements. Our experimental results demonstrate that such adaptive data dissemination is essential to meet diverse temporal coherency requirements, to be resilient to failures, and for the efficient and scalable utilization of server and network resources.