9264591
Exactly-once semantics in a replicated messaging system
A wide-area distributed message delivery system can use replication to improve performance and availability. However, without safeguards, replicated messages may be delivered to a mobile device more than once, making the device's user repeat actions (e.g. making unnecessary phone calls, firing weapons repeatedly). Alternatively, they may not be delivered at all, making the user miss important messages. In this paper, we address the problem of exactly-once delivery to mobile clients when messages are replicated globally. We define exactly-once semantics and propose algorithms to guarantee it. We also propose and define a relaxed version of exactly-once semantics which is appropriate for limited-capability mobile devices. We study the relative performance of our algorithms compared to the weaker at-least-once semantics, and find that the performance overhead of exactly-once can be minimized in most cases by careful design of the system.
39326914
Parameterized subscriptions in publish/subscribe systems
Traditional publish/subscribe systems commonly deal with static subscriptions, whose event filtering criteria stay fixed once defined. Although systems with static subscriptions are simpler to implement, there are cases where the subscription criterion involves state that changes frequently over time. Rather than having the user re-submit his/her subscription repeatedly, we propose parameterized subscriptions as a systematic solution for adaptive subscriptions. Parameterized subscriptions depend on one or more parameters, which are state variables stored and maintained automatically by the publish/subscribe servers. In this paper, we modify traditional publish/subscribe protocols in order to deal with parameterized subscriptions. We then look at certain optimizations that improve the efficiency by controlling where and how much state is allocated in the system. Finally, we present a simple evaluation framework to illustrate the fundamental operating differences between several schemes.
14183146
Replicated condition monitoring
A condition monitoring system tracks real-world variables and alerts users when a predefined condition becomes true, e.g., when stock price drops, or when a nuclear reactor over-heats. Replication of monitoring servers can reduce the probability that an important alert is missed. However, replicated independent servers can sometimes report “conflicting” alerts to the user, causing confusion. In this paper, we study the problem of replicated condition monitoring. We identify and formally define three desirable properties of a replicated system, namely, orderedness, consistency, and completeness. We propose new monitoring algorithms that enforce some or all of the desired properties in different scenarios.
8223639
Assignment-based partitioning in a condition monitoring system
A condition monitoring system tracks real-world variables and alerts users when a predefined condition becomes true, e.g., when enemy planes take off, or when suspicious terrorist activities and communication are detected. However, a monitoring server can easily get overwhelmed by rapid incoming data. To prevent this, we partition the condition being monitored and distribute the workload onto several independent servers. In this paper, we study the problem of how to make a partitioned system behave “equivalently” to a one-server system. We identify and formally define three desirable properties of a partitioned system, namely, orderedness, consistency, and completeness. We propose assignment-based partitioning as a solution that can handle opaque conditions and simplifies load balancing. We also look at a few typical partitioned systems, and discuss their merits using several metrics that we define. Finally, an algorithm is presented to reduce complex system configurations to simpler ones.
32904961
Assignment-Based Partitioning in a Condition Monitoring System
A condition monitoring system tracks real-world variables and alerts users when a predefined condition becomes true, e.g., when enemy planes take off, or when suspicious terrorist activities and communication are detected. However, a monitoring server can easily get overwhelmed by rapid incoming data. To prevent this, we partition the condition being monitored and distribute the workload onto several independent servers. In this paper, we study the problem of how to make a partitioned system behave "equivalently" to a one-server system. We identify and formally define three desirable properties of a partitioned system, namely, orderedness, consistency, and completeness. We propose assignment-based partitioning as a solution that can handle opaque conditions and simplifies load balancing. We also look at a few typical partitioned systems, and discuss their merits using several metrics that we define. Finally, an algorithm is presented to reduce complex system configurations to simpler ones.
26991946
Publish/Subscribe in a Mobile Environment
A publish/subscribe system dynamically routes and delivers events from sources to interested users, and is an extremely useful communication service when it is not clear in advance who needs what information. In this paper we discuss how a publish/subscribe system can be extended to operate in a mobile environment, where events can be generated by moving sensors or users, and subscribers can request delivery at handheld and/or mobile devices. We describe how the publish/subscribe system itself can be distributed across multiple (possibly mobile) computers to distribute load, and how the system can be replicated to cope with failures, message loss, and disconnections.
1232229
Active Mining of Data Streams
Most previously proposed mining methods on data streams make an unrealistic assumption that “labelled” data stream is readily available and can be mined at anytime. However, in most real-world problems, labelled data streams are rarely immediately available. Due to this reason, models are refreshed periodically, that is usually synchronized with data availability schedule. There are several undesirable consequences of this “passive periodic refresh”. In this paper, we propose a new concept of demand-driven active data mining. It estimates the error of the model on the new data stream without knowing the true class labels. When significantly higher error is suspected, it investigates the true class labels of a selected number of examples in the most recent data stream to verify the suspected higher error. 1 State-of-the-art Stream Mining State-of-the-art work on mining data streams concentrates on capturing time-evolving trends and patterns with “labeled” data. However, one important aspect that is often ignored or unrealistically assumed is the availability of “class labels” of data streams. Most algorithms make an implicit and impractical assumption that labeled data is readily available. Most works focus on how to detect the change in patterns and how to update the model to reflect such changes when there are “labelled” instances to be learned. However, for many applications, the class labels are not “immediately” available unless dedicated efforts and substantial costs are spent to investigate these labels right away. If the true class labels were readily available, data mining models would not be very useful we might just wait. In credit card fraud detection, we usually do not know if a particular transaction is a fraud until at least one month later after the account holder receives and reviews the monthly statement. Due to these facts, most current applications obtain class labels and update existing models in preset frequency, usually synchronized with data refresh. The effectiveness of the passive mode is dictated by some “statuary and static constraints”, yet not by the “demand” for a better model with a lower loss. Such a passive mode to mine data streams results in a number of potential undesirable consequences that contradict the notions of “streaming” and “continuous”. First, it may incur possibly higher loss due to neglected pattern drifts. If either the concept or data distribution drifts rapidly at an un-forecasted rate that statuary constraints do not catch up, the models are likely to be out-of-date on the data stream and important business opportunities might be missed. Second, it may have unnecessary model refresh. If there is neither conceptual nor distributional change, periodic passive model refresh and re-validation is a waste of resources. 1.1 Demand-driven Active Mining of Data Streams We are proposing a demand-driven active stream data mining process that solves the problems of passive stream data mining. As a summary, our particular implementation of active stream data mining has three simple steps: 1. Detect potential changes of data streams “on the fly” when the existing model classifies continuous data streams. The detection process does not use or know any true labels of the stream. One of the change detection methods is a “guess” of the actual loss or error rate of the model on the new data stream. 2. If the guessed loss or error rate of the model in step 1 is much higher than an application-specific tolerable maximum, we choose a small number of data records in the new data stream to investigate their true class labels. With these true class labels, we statistically estimate the true loss of the model. 3. If the statistically estimated loss in step 2 is verified to be higher than the tolerable maximum, we reconstruct the old model by using the same true class labels sampled in the previous step. In this paper, we concentrate on the first two steps. Our particular implementation extends on classification trees.
18171732
Data Placement in Intermittently Available Environments
In this paper, we address the problem of data placement in a grid based multimedia environment, where the resource providers, i.e. servers, are intermittently available. The goal is to optimize the system performance by admitting maximum number of users into the system while ensuring user Quality of Service (QoS). We define and formulate various placement strategies that determine the degree of replication necessary for video objects by using a cost-based optimization procedure based on predictions of expected requests under various time-map scenarios and QoS demands. We also devise methods for dereplication of videos based on changes in popularity and server usage patterns. Our performance results indicate the benefits obtained the judicious use of dynamic placement strategies.
11427190
Exploiting the IPID Field to Infer Network Path and End-System Characteristics
In both active and passive network Internet measurements, the IP packet has a number of important header fields that have played key roles in past measurement efforts, e.g., IP source/destination address, protocol, TTL, port, and sequence number/acknowledgment. The 16-bit identification field (IPID) has only recently been studied to determine what information it might yield for network measurement and performance characterization purposes. We explore several new uses of the IPID field, including how it can be used to infer: (a) the amount of internal (local) traffic generated by a server; (b) the number of servers in a large-scale, load-balanced server complex and; (c) the difference between one-way delays of two machines to a target computer. We illustrate and validate the use of these techniques through empirical measurement studies.
1605324
Using contextual information to improve retrieval performance
In this paper, we propose a contextual retrieval framework which incorporates the user and global contextual information into the probabilistic retrieval model. We investigate different techniques of using contextual information to improve information retrieval performance in details. In particular, (1) we use the related text contextual information for query expansion; (2) we use the granularity information to construct the document level index and paragraph level index; (3) we use the geographic information for filtering. In addition, a new term weighting function BM5O is proposed based on the global context information. This framework is adaptable and extensible. If there is a new context category, we can extend the existing search system to accommodate it. Finally, we report our experimental findings on TREC data sets.
31363261
Evaluation of Distance Measures for Partial Image Retrieval Using Self-Organizing Map
Digital image libraries are becoming more common and widely used as more visual information is produced at a rapidly growing rate. With this immense growth, there is a need to organize and index these databases so that we can efficiently retrieve the desired images. In this paper, we evaluate the performance of the self-organising maps (SOMs) with different distance measures in retrieving similar images when a full or a partial query image is presented to the SOM. Our method makes use of RGB colour histograms. As the RGB colour space is very large, another SOM is employed to adaptively quantise the colour space prior to generating the histograms. Some promising results are reported.