10264816
Stochastically Consistent Caching and Dynamic Duty Cycling for Erratic Sensor Sources
We present a novel dynamic duty cycling scheme to maintain stochastic consistency for caches in sensor networks. To reduce transmissions, base stations often maintain caches for erratically changing sensor sources. Stochastic consistency guarantees the cache-source deviation is within a pre-specified bound with a certain confidence level. We model the erratic sources as Brownian motions, and adaptively predict the next cache update time based on the model. By piggybacking the next update time in each regular data packet, we can dynamically adjust the relaying nodes' duty cycles so that they are awake before the next update message arrives, and are sleeping otherwise. Through simulations, we show that our approach can achieve very high source-cache fidelity with low power consumption on many real-life sensor data. On average, our approach consumes 4-5 times less power than GAF [1], and achieves 50% longer network lifetime.
18818332
Adaptive data broadcasting in asymmetric communication environments
We present a new adaptive broadcast dissemination model to support flexible responses to client requests. Several features distinguish our model. First, client queries do not target individual documents, but specify the required information by attributes. Second, clients are satisfied by responses that are sufficiently close to the desired information. Finally, the server in our model solicits randomized feedback from clients to adapt its broadcast program to client needs. Our simulation results show that our model captures the interest patterns of clients more efficiently and more accurately and scales very well with the number of clients, while reducing overall client average waiting times.
16374313
A self-tuning page cleaner for DB2
The buffer pool in a DBMS is used to cache the disk pages of the database. Because typical database workloads are I/O-bound, the effectiveness of the buffer pool management algorithm is a crucial factor in the performance of the DBMS. In IBM's DB2 buffer pool, the page cleaning algorithm is used to write changed pages to disks before they are selected for replacement. We conducted a detailed study of page cleaning in DB2 version 7.1.0 for Windows by both trace-driven simulation and measurements. Our results show that system throughput can be increased by 19% when the page cleaning algorithm is carefully tuned. In practice, however the manual tuning of this algorithm is difficult. A self-tuning algorithm for page cleaning is proposed posed in this paper to automate this tuning task. Simulation results show that the self-tuning algorithm can achieve performance comparable to the best manually tuned system.
19868872
Management Assistance for Work First via a Dynamic Website
The purpose of the Work First project is to assist the state Dept. of Health and Human Services (DHHS) staff and each county Dept. of Social Services (DSS) across the state in the management of welfare reform initiatives. Work First is the cash assistance program in NC. One important objective is to encourage and support program managers to use data on outcomes to assess their performance. This is accomplished by providing them with easy to use and easy to understand comprehensive information on the experiences of families and individuals that have received assistance through their agency over time.
14320982
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized information is forwarded to the global mining process. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple data sources. In this paper, we present an alternative local mining approach for finding sequential patterns in the local databases of a multi-database. We propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. Approximate sequential patterns can effectively summerize and represent the local databases by identifying the underlying trends in the data. We present a novel algorithm, ApproxMAP, to mine approximate sequential patterns, called consensus patterns, from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We conduct an extensive and systematic performance study over synthetic and real data. The results demonstrate that ApproxMAP is effective and scalable in mining large sequences databases with long patterns. Hence, ApproxMAP can efficiently summarize a local database and reduce the cost for global mining. Furthremore, we present an elegant and uniform model to identify both high vote sequential patterns and exceptional sequential patterns from the collection of these consensus patterns from each local databases.
8299246
HyLog: A High Performance Approach to Managing Disk Layout
Our objective is to improve disk I/O performance in multi-disk systems supporting multiple concurrent users, such as file servers, database servers, and email servers. In such systems, many disk reads are absorbed by large in-memory buffers, and so disk writes comprise a large portion of the disk I/O traffic. LFS (Log-structured File System) has the potential to achieve superior write performance by accumulating small writes into large blocks and writing them to new places, rather than overwriting on top of their old copies (called Overwrite). Although it is commonly believed that the high segment cleaning overhead of LFS makes it a poor choice for workloads with random updates, in this paper we find that because of the fast improvement of disk technologies, LFS significantly outperforms Overwrite in a wide range of system configurations and workloads (including the random update workload) under modern and future disks. 
 
LFS performs worse than Overwrite, however, when the disk space utilization is very high due to the high cleaning cost. In this paper, we propose a new approach, the Hybrid Log-structured (Hy-Log) disk layout, to overcome this problem. Hy-Log uses a log-structured approach for hot pages to achieve high write performance, and Overwrite for cold pages to reduce the cleaning cost. We compare the performance of HyLog to that of Overwrite, LFS and WOLF (the latest improvement on LFS) under various system configurations and workloads. Our results show that, in most cases, Hylog performs comparably to the best of the other three approaches.
7026910
An Approach to Active Spatial Data Mining Based on Statistical Information
Spatial data mining presents new challenges due to the large size of spatial data, the complexity of spatial data types, and the special nature of spatial access methods. Most research in this area has focused on efficient query processing of static data. This paper introduces an active spatial data mining approach that extends the current spatial data mining algorithms to efficiently support user-defined triggers on dynamically evolving spatial data. To exploit the locality of the effect of an update and the nature of spatial data, we employ a hierarchical structure with associated statistical information at the various levels of the hierarchy and decompose the user-defined trigger into a set of subtriggers associated with cells in the hierarchy. Updates are suspended in the hierarchy until their cumulative effect might cause the trigger to fire. It is shown that this approach achieves three orders of magnitude improvement over the naive approach that reevaluate the condition over the database for each update, while both approaches produce the same result without any delay. Moreover, this scheme can support incremental query processing as well.