14448318
On Suspending and Resuming Dataflows
Consider a long-running, resource-intensive query Q running on a database management system (DBMS). Suppose another task T with much higher priority arrives, and we need to process T as quickly as possible and with all available resources. Ideally, the system should suspend the execution of Q, quickly release all resources held by Q, and start T using all resources. Query Q can be resumed once T finishes execution, ideally without losing any significant fraction of the work that Q had done prior to suspend. Beyond supporting mixed-priority DBMS workloads, suspend/resume of queries, or dataflows in general, is important in many other settings: Utility and grid: Dataflows now run frequently on computational utilities and grids composed of autonomous resources. When the owner of resources wants to use them, dataflows running on these resources must release control quickly, and migrate to other resources. Software rejuvenation: Benefits of software rejuvenation, the practice of rebooting enterprise computing systems regularly, are now recognized widely. Reboot is critical when performance degrades due to resource exhaustion caused by resource leaks. Suspend and resume is important in this setting because (1) the challenge of predicting completion times accurately makes it difficult to schedule task completion to match a rejuvenation schedule; (2) when performance degrades, it may not be cost-effective to wait for all dataflows to complete before rebooting.
7776820
Scalable continuous query processing by tracking hotspots
This paper considers the problem of scalably processing a large number of continuous queries. We propose a flexible framework with novel data structures and algorithms for group-processing and indexing continuous queries by exploiting potential overlaps in query predicates. Our approach partitions the collection of continuous queries into groups based on the clustering patterns of the query ranges, and then applies specialized processing strategies to those heavily-clustered groups (or hotspots). To maintain the partition dynamically, we present efficient algorithms that maintain a nearly optimal partition in nearly amortized logarithmic time. We show how to use the hotspots to scalably process large numbers of continuous select-join and band-join queries, which are much more challenging than simple range selection queries. Experiments demonstrate that this approach can improve the processing throughput by orders of magnitude. As another application of hotspots, we show how to use them to build a high-quality histogram for intervals in linear time.
11503542
Monitoring Continuous Band-Join Queries over Dynamic Data
A continuous query is a standing query over a dynamic data set whose query result needs to be constantly updated as new data arrive. We consider the problem of constructing a data structure on a set of continuous band-join queries over two data sets R and S, where each band-join query asks for reporting the set { (r,s)∈ R× S | a≤ r–s≤ b} for some parameters a and b, so that given a data update in R or S, one can quickly identify the subset of continuous queries whose results are affected by the update, and compute changes to these results. 
 
We present the first nontrivial data structure for this problem that simultaneously achieves subquadratic space and sublinear query time. This is achieved by first decomposing the original problem into two independent subproblems, and then carefully designing data structures suitable for each case, by exploiting the particular structure in each subproblem. 
 
A key step in the above construction is a data structure whose performance increases with the degree of clusteredness of the band-joins being indexed. We believe that this structure is of independent interest and should have broad impact in practice. We present the details in [1].
214792426
TIP: a temporal extension to Informix
Commercial relational database systems today provide only limited temporal support. To address the needs of applications requiring rich temporal data and queries, we have built TIP (Temporal Information Processor), a temporal extension to the Informix database system based on its DataBlade technology. Our TIP DataBlade extends Informix with a rich set of datatypes and routines that facilitate temporal modeling and querying. TIP provides both C and Java libraries for client applications to access a TIP-enabled database, and provides end-users with a GUI interface for querying and browsing temporal data.
2866284
BLINKS: ranked keyword searches on graphs
Query processing over graph-structured data is enjoying a growing number of applications. A top-k keyword search query on a graph finds the top k answers according to some ranking criteria, where each answer is a substructure of the graph containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, we propose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs. BLINKS follows a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search. To reduce the index space, BLINKS partitions a data graph into blocks: The bi-level index stores summary information at the block level to initiate and guide search among blocks, and more detailed information for each block to accelerate search within blocks. Our experiments show that BLINKS offers orders-of-magnitude performance improvement over existing approaches.
12872505
Value-Based Notification Conditions in Large-Scale Publish/Subscribe Systems
We address the problem of providing scalable support for subscriptions with personalized value-based notification conditions in wide-area publish/subscribe systems. Notification conditions can be fine-tuned by subscribers, allowing precise and flexible control of when events are delivered to the subscribers. For example, a user may specify that she should be notified if and only if the price of a particular stock moves outside a "radius" around her last notified value. Naive techniques for handling notification conditions are not scalable. It is challenging to share subscription processing and notification dissemination of subscriptions with personalized value-based notification conditions, because two subscriptions may see two completely different sequences of notifications even if they specify the same radius. We develop and experimentally evaluate scalable processing and dissemination techniques for these subscriptions. Our approach uses standard network substrates for notification dissemination, and avoids pushing complex application processing into the network. Compared with other alternatives, our approach generates orders of magnitude lower network traffic, and incurs lower server processing cost.
2868377
Intelligent decision system and its application in business innovation self assessment
In this paper, it is described how a multiple criteria decision analysis software tool, the Intelligent Decision System (IDS), can be used to help business self-assessment. Following a brief outline of a model for assessing business innovation capability and the IDS software, the process of using IDS to implement different types of assessment questions is discussed. It is demonstrated that IDS is a flexible tool capable of handling different types of data in self-assessment, including uncertain and incomplete data, and providing a wide range of information including scores, performance diversity, strength and weakness profile and graphics.
8294856
Clustering moving objects
Due to the advances in positioning technologies, the real time information of moving objects becomes increasingly available, which has posed new challenges to the database research. As a long-standing technique to identify overall distribution patterns in data, clustering has achieved brilliant successes in analyzing static datasets. In this paper, we study the problem of clustering moving objects, which could catch interesting pattern changes during the motion process and provide better insight into the essence of the mobile data points. In order to catch the spatial-temporal regularities of moving objects and handle large amounts of data, micro-clustering [20] is employed. Efficient techniques are proposed to keep the moving micro-clusters geographically small. Important events such as the collisions among moving micro-clusters are also identified. In this way, high quality moving micro-clusters are dynamically maintained, which leads to fast and competitive clustering result at any given time instance. We validate our approaches with a through experimental evaluation, where orders of magnitude improvement on running time is observed over normal K-Means clustering method [14].
15187378
An efficient fault-tolerant distributed channel allocation algorithm for cellular networks
A channel allocation algorithm in a cellular network consists of two parts: a channel acquisition algorithm and a channel selection algorithm. Some of the previous works in this field focused on centralized approaches to allocating channels. But, centralized approaches are neither scalable nor reliable. Recently, distributed dynamic channel allocation algorithms have been proposed, and they have gained a lot of attention due to their high reliability and scalability. But, in most of the algorithms, the cell that wants to borrow a channel has to wait for replies from all its interference neighbors and, hence, is not fault-tolerant. In this paper, we propose a new algorithm that is fault-tolerant and makes full use of the available channels. It can tolerate the failure of mobile nodes as well as static nodes without any significant degradation in service.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
32074986
Statistical Neighbor Distance Influence in Active Contours
In this paper, we propose a new model for segmentation of images containing multiple objects. In order to take advantage of the constraining information provided by neighboring objects, we incorporate information about the relative position and shape of neighbors into the segmentation process by defining a new "distance" term into the energy functional. We introduce a representation for relative neighbor distances, and define a probability distribution over the variances of the relative neighbor distances of a set of training images. By minimizing the energy functional, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the relative distance information and the image grey level information. Several objects in an image can be automatically detected simultaneously.