2325109
Exploiting upper and lower bounds in top-down query optimization
System R's bottom-up query optimizer architecture forms the basis of most current commercial database managers. The paper compares the performance of top-down and bottom-up optimizers, using the measure of the number of plans generated during optimization. Top down optimizers are superior according to this measure because they can use upper and lower bounds to avoid generating groups of plans. Early during the optimization of a query, a top-down optimizer can derive upper bounds for the costs of the plans it generates. These bounds are not available to typical bottom-up optimizers since such optimizers generate and cost all subplans before considering larger containing plans. These upper bounds can be combined with lower bounds, based solely on logical properties of groups of logically equivalent subqueries, to eliminate entire groups of plans from consideration. We have implemented such a search strategy, in a top-down optimizer called Columbia. Our performance results show that the use of these bounds is quite effective, while preserving the optimality of the resulting plans. In many circumstances this new search strategy is even more effective than heuristics such as considering only left deep plans.
2930673
Adaptive Security Dialogs for Improved Security Behavior of Users
Despite the increasing awareness of the importance of security for daily computer users, we see that many users still fail to behave securely when confronted with a security-related decision. In this paper, we introduce a new approach to security-related dialogs called Adaptive Security Dialogs (ASD). This approach is a combination of a new architecture and a new way of interacting with users to provide them with appropriate and effective security dialogs. ASD realizes this goal by matching the complexity and intrusiveness of security-related dialogs to the risk associated with the decision the user is making. This results in an architecture in which users can focus on their tasks, get (immediate) feedback on their decisions, and interact with dialogs with an appropriate complexity and appearance for the decision's associated risk. This paper makes the following three contributions. First, we introduce a general architecture for handling security-related decisions. Second, through an empirical user study using a web-based e-mail client, we show significant improvement in the care exercised by our participants without sacrificing usability. Third, we describe how the different pieces of existing research fit into the bigger picture of improving users' behavior.
5930637
Parallelization of IBM mambo system simulator in functional modes
Mambo [4] is IBM's full-system simulator which models PowerPC systems, and provides a complete set of simulation tools to help IBM and its partners in pre-hardware development and performance evaluation for future systems. Currently Mambo simulates target systems on a single host thread. When the number of cores increases in a target system, Mambo's simulation performance for each core goes down. As the so-called "multi-core era" approaches, both target and host systems will have more and more cores. It is very important for Mambo to efficiently simulate a multi-core target system on a multi-core host system. Parallelization is a natural method to speed up Mambo under this situation.
 Parallel Mambo (P-Mambo) is a multi-threaded implementation of Mambo. Mambo's simulation engine is implemented as a user-level thread-scheduler. We propose a multi-scheduler method to adapt Mambo's simulation engine to multi-threaded execution. Based on this method a core-based module partition is proposed to achieve both high inter-scheduler parallelism and low inter-scheduler dependency. Protection of shared resources is crucial to both correctness and performance of P-Mambo. Since there are two tiers of threads in P-Mambo, protecting shared resources by only OS-level locks possibly introduces deadlocks due to user-level context switch. We propose a new lock mechanism to handle this problem. Since Mambo is an on-going project with many modules currently under development, co-existence with new modules is also important to P-Mambo. We propose a global-lock-based method to guarantee compatibility of P-Mambo with future Mambo modules.
 We have implemented the first version of P-Mambo in functional modes. The performance of P-Mambo has been evaluated on the OpenMP implementation of NAS Parallel Benchmark (NPB) 3.2 [12]. Preliminary experimental results show that P-Mambo achieves an average speedup of 3.4 on a 4-core host machine.
31976355
The Tradeoff of Delta Table Merging and Re-writing Algorithms in What-If Analysis Application
What-if analysis can provide more meaningful information than classical OLAP. Multi-scenario hypothesis based on historical data needs efficient what-if data view support. In general, delta table for what-if analysis is more general than other solutions such as query re-writing prototype of Sesame. Delta table is independent of base table and is more suitable to represent complex hypothetical updates and multi-version hypothetical updates. Due to low efficiency of traditional delta table merging algorithm which is based on set operation, there are few researches focus on delta table merging algorithm but on query re-writing algorithm. By analyzing the difference between what-if query and what-if analysis and improving the delta table merging algorithm, we propose novel algorithms without set operation of difference. Considering the feature of aggregate operations in OLAP analysis, pre-merge algorithm is presented without the generation of what-if data view before group-bys in the scenario of SUM, AVERAGE and COUNT function OLAP queries. In our experiments, the pre-merge algorithm greatly improved the efficiency of delta table merging procedure which is close to base group-by statement and superior to the re-writing algorithm. A complete comparison between all candidate delta table merging algorithms and re-writing algorithm with different what-if update conditions of update, deletion, insertion and mixed what-if updates is exhibited in our experiments, the policy of what-if analysis among different types is also discussed.
21616351
Palmprint Recognition Using a Novel Sparse Coding Technique
This paper proposes a novel recognition method for palmprints using a new sparse coding (SC) algorithm proposed by us. This algorithm exploited the maximum Kurtosis as the sparse measure criterion, at one time, a fixed variance term of sparse coefficients is used to yield a fixed information capacity. Experimental results show that the feature basis vectors of palmprint images can be successfully extracted by using our SC algorithm. Using the radial basis probabilistic neural network (RBPNN), the classification task can be implemented easily. Finally, compared with methods of principal component analysis (PCA) and the classical SC, simulation results show that our algorithm is indeed efficient and effective in performing palmprint recognition task.
18873066
Extending Online Travel Agency with Adaptive Reservations
Current online ticket booking systems either do not allow customers to reserve a ticket with a locked price, or grant a fixed reservation timespan, typically 24 hours. The former often leads to false availability: when a customer decides to purchase a ticket after a few queries, she finds that either the ticket is no longer available or the price has hiked up. The latter, on the other hand, may result in unnecessary holdback: a customer cannot purchase a ticket because someone else is holding it, who then cancels the reservation after an excessively long period of time. False availability and holdback routinely lead to loss of revenues, credibility and above all, customers. To rectify these problems, this paper introduces a transaction model for e-ticket systems to support a reservation functionality: customers can reserve tickets with a locked price, for a timespan that is determined by the demands on the tickets, rather than being fixed for all kinds of the tickets. We propose a method for implementing the model, based on hypothetical queries and triggers. We also show how to adjust the reservation timespan w.r.t. demands. We experimentally verify that our model and methods effectively reduce both false availability and holdback rates. These yield a practical approach to improving not only e-ticket systems but also other e-commerce systems.
43457446
Synthesis of facial expressions using a 3D anatomical model
In this paper we propose an individualized 3D facial model constructed from the anatomical perspective for realistic facial expression synthesis. Our model incorporates a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The skin is modeled as a multi-layer mass-spring lattice by taking into account the nonlinear stress-strain relationship of the real skin. The 3D face model incorporates a skull structure which extends the scope of facial motion and facilitates facial muscle definition. The automatic facial muscle construction is achieved by using an efficient muscle mapping approach that ensures different muscles to be located at the anatomically correct positions between the skin and skull layers. For computational efficiency, we devise a semi-implicit integration method to numerically solve the governing dynamics. The dynamic facial animation algorithm runs at an interactive rate with realistic facial expressions to be synthesized.
6483143
Using Proximity in Query Focused Multi-document Extractive Summarization
The query focused multi-document summarization tasks usually tend to answer the queries in the summary. In this paper, we suggest introducing an effective feature which can represent the relation of key terms in the query. Here, we adopt the feature of term proximity commonly used in the field of information retrieval, which has improved the retrieval performance according to the relative position of terms. To resolve the problem of data sparseness and to represent the proximity in the semantic level, concept expansion is conducted based on WordNet. By leveraging the term importance, the proximity feature is further improved and weighted according to the inverse term frequency of terms. The experimental results show that our proposed feature can contribute to improving the summarization performance.