6059000
Preserving User Location Privacy in Mobile Data Management Infrastructures
Location-based services, such as finding the nearest gas station, require users to supply their location information. However, a user's location can be tracked without her consent or knowledge. Lowering the spatial and temporal resolution of location data sent to the server has been proposed as a solution. Although this technique is effective in protecting privacy, it may be overkill and the quality of desired services can be severely affected. In this paper, we suggest a framework where uncertainty can be controlled to provide high quality and privacy-preserving services, and investigate how such a framework can be realized in the GPS and cellular network systems. Based on this framework, we suggest a data model to augment uncertainty to location data, and propose imprecise queries that hide the location of the query issuer and yields probabilistic results. We investigate the evaluation and quality aspects for a range query. We also provide novel methods to protect our solutions against trajectory-tracing. Experiments are conducted to examine the effectiveness of our approaches.
13946371
Self-Learning Disk Scheduling
Performance of disk I/O schedulers is affected by many factors, such as workloads, file systems, and disk systems. Disk scheduling performance can be improved by tuning scheduler parameters, such as the length of read timers. Scheduler performance tuning is mostly done manually. To automate this process, we propose four self-learning disk scheduling schemes: change-sensing Round-Robin, feedback learning, per-request learning, and two-layer learning. experiments show that the novel two-layer learning scheme performs best. It integrates the workload-level and request-level learning algorithms. It employs feedback learning techniques to analyze workloads, change scheduling policy, and tune scheduling parameters automatically. We discuss schemes to choose features for workload learning, divide and recognize workloads, generate training data, and integrate machine learning algorithms into the two-layer learning scheme. We conducted experiments to compare the accuracy, performance, and overhead of five machine learning algorithms: decision tree, logistic regression, naive Bayes, neural network, and support vector machine algorithms. Experiments with real-world and synthetic workloads show that self-learning disk scheduling can adapt to a wide variety of workloads, file systems, disk systems, and user preferences. It outperforms existing disk schedulers by as much as 15.8% while consuming less than 3%-5% of CPU time.
10877615
A Decision Procedure for XPath Satisfiability in the Presence of DTD Containing Choice
XPath satisfiability is one of the most basic problems of XML query optimization. A satisfiability decision framework, named SAT-DTD, is proposed to determine, given a set of XPath queries P and a DTD τ, which subset of P are satisfiable by an XML tree conforming to DTD τ. In the framework, an indexed NFA is constructed from the set of XPath queries P, and then the NFA is driven by simple API for DTD (SAD, something like SAX) events, derived from DTD τ, to evaluate the predicates in P and to decide the satisfiability of P. Especially, DTD choice (i.e. '|' operator) is taken into consideration, and an algorithm, named SAT-DTD_C, which bases on SAT-DTD, is put forward to determine the unsatisfiability caused by DTD choice. At last, the complexity of the algorithms is analyzed, and the correctness of the algorithms is tested by experiments.
4002740
Selectivity Estimation by Batch-Query based Histogram and Parametric Method
Histograms are used extensively for selectivity estimation and approximate query processing. Workload-aware dynamic histograms can self-tune itself based on query feedback without scanning or sampling the underlaying datasets in a systematic and comprehensive way. Dynamic histograms allocate more buckets not only for the areas with most skewed data distribution but also according to users' interest. However, it takes long time to 'warm-up' (i.e., a large number of queries need to be processed before the histogram can provide a satisfactory coverage and accuracy). Thus, it is less effective to adapt with workload pattern changes. In this paper, we propose a novel online query scheduling algorithm which can significantly reduce the warm-up time for dynamic histograms. A parametric method is proposed to remedy the problem of inaccurate query selectivity estimation for the areas with poor histogram coverage. Experimental results demonstrate a significant effectiveness and accuracy improvement of our approach.
18862565
Inferring Gene Regulatory Networks from Multiple Data Sources Via a Dynamic Bayesian Network with Structural EM
Using our dynamic Bayesian network with structural Expectation Maximization (SEM-DBN), we develop a new framework to model gene regulatory network from both gene expression data and transcriptional factor binding site data. Only based on mRNA expression data, it is not enough to accurately estimate a gene network. It is difficult for us to estimate a gene network accurately only with the mRNA expression data. In this paper, we use the transcription factor binding location data in order to introduce the prior knowledge to SEM-DBN model. Gene expression data are also exploited specifically for likelihood. Meanwhile, we incorporate the prior knowledge into every learning step by SEM rather than only learning from the very beginning, which can compensate the attenuation of the effect with location data. The effectiveness of our proposed method is demonstrated through the analysis of Saccharomyces cerevisiae cell cycle data. The combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from only gene expression data alone.
12120268
Performance of Coded pi/2 NS-8QAM Modulation
Spectrum and Bit Error Rate (BER) performance of a coded π/2 phase shift Non-Square (NS) 8 Quadrature Amplitude Modulation (QAM) system are studied in this letter. The modulation process of this scheme removes all 180° phase shift between adjacent constellation points and contains inherent memory which can be treated as a type of inner coding in coded system. Simulation results show that this modulation scheme has much lower spectrum regrowth and better BER performance when passing through nonlinear channel compared with conventional mode.
2560128
Towards Human Decision-Making in Multi-Agent Systems
2 Decision theory finds its roots as a formal topic of research in the fields of economics and psychology. It traditionally relied upon the formal description of decision making embodied principle of maximum expected utility (EUT). But EUT can not capture the common human decision-making attitudes because it does not model the domain of human intuitions – thoughts or preferences that come to mind quickly and without much reflection. This paper models one human’s intuitive attitudes toward risk: risk-aversive and risk-seeking. Rather than immediately making a choice directly from the set of alternatives, agents first form an attitude concerning each possible alternative and likewise solve the corresponding decision problem with a preference order. Correspondingly, this preference order should not be constructed solely on the basis of the individual agent’s own attitudes but with the attitudes of all agents which are relevant. This paper presents our work on above two issues: capturing human intuitive decision-making under risk and extending it to social settings. Finally, we present two experiments that explore risk management behaviors of agents in social settings.