Top-<Emphasis Type="Italic">k</Emphasis> temporal keyword search over social media data

Social media services have already become main sources for monitoring emerging topics and sensing real-life events. A social media platform manages social stream consisting of a huge volume of timestamped user generated data, including original data and repost data. However, previous research on keyword search over social media data mainly emphasizes on the recency of information. In this paper, we first propose a problem of top-k most significant temporal keyword query to enable more complex query analysis. It returns top-k most popular social items that contain the keywords in the given query time window. Then, we design a temporal inverted index with two-tiers posting list to index social time series and a segment store to compute the exact social significance of social items. Next, we implement a basic query algorithm based on our proposed index structure and give a detailed performance analysis on the query algorithm. From the analysis result, we further refine our query algorithm with a piecewise maximum approximation (PMA) sketch. Finally, extensive empirical studies on a real-life microblog dataset demonstrate the combination of two-tiers posting list and PMA sketch achieves remarkable performance improvement under different query settings.     

Enhancing comparison shopping agents through ordering and gradual information disclosure

The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, and consequently decreases each CSAs’ expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers’ prices, potentially resulting in a more attractive “best price”. In this paper we suggest a complementary approach that improves the attractiveness of the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer’s tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.     

Big data in the public sector: Uncertainties and readiness

Big data is being implemented with success in the private sector and science. Yet the public sector seems to be falling behind, despite the potential value of big data for government. Government organizations do recognize the opportunities of big data but seem uncertain about whether they are ready for the introduction of big data, and if they are adequately equipped to use big data. This paper addresses those uncertainties. It presents an assessment framework for evaluating public organizations’ big data readiness. Doing so demystifies the concept of big data, as it is expressed in terms of specific and measureable organizational characteristics. The framework was tested by applying it to organizations in the Dutch public sector. The results suggest that organizations may be technically capable of using big data, but they will not significantly gain from these activities if the applications do not fit their organizations and main statutory tasks. The framework proved helpful in pointing out areas where public sector organizations could improve, providing guidance on how government can become more big data ready in the future.     

When do agents outperform centralized algorithms?

Multi-agent systems (MAS) literature often assumes decentralized MAS to be especially suited for dynamic and large scale problems. In operational research, however, the prevailing paradigm is the use of centralized algorithms. Present paper empirically evaluates whether a multi-agent system can outperform a centralized algorithm in dynamic and large scale logistics problems. This evaluation is novel in three aspects: (1) to ensure fairness both implementations are subject to the same constraints with respect to hardware resources and software limitations, (2) the implementations are systematically evaluated with varying problem properties, and (3) all code is open source, facilitating reproduction and extension of the experiments. Existing work lacks a systematic evaluation of centralized versus decentralized paradigms due to the absence of a real-time logistics simulator with support for both paradigms and a dataset of problem instances with varying properties. We extended an existing logistics simulator to be able to perform real-time experiments and we use a recent dataset of dynamic pickup-and-delivery problem with time windows instances with varying levels of dynamism, urgency, and scale. The OptaPlanner constraint satisfaction solver is used in a centralized way to compute a global schedule and used as part of a decentralized MAS based on the dynamic contract-net protocol (DynCNET) algorithm. The experiments show that the DynCNET MAS finds solutions with a relatively lower operating cost when a problem has all following three properties: medium to high dynamism, high urgency, and medium to large scale. In these circumstances, the centralized algorithm finds solutions with an average cost of 112.3% of the solutions found by the MAS. However, averaged over all scenario types, the average cost of the centralized algorithm is 94.2%. The results indicate that the MAS performs best on very urgent problems that are medium to large scale.     

磁盘空间占用“大户”查看我最行

当计算机系统长时间运行之后,很多人或许会感觉到系统速度会越来越慢,硬盘剩余空间也越来越小了。特别是一些喜欢下载软件或欣赏视频的用户,常常会感觉到硬盘空间就像是被病毒侵蚀一样不知不觉就被消耗光了。     

识别并管理企业的应用个性

很多时候,IT预算只是用来管理并运行企业的基础设施,而不是推动业务创新。不能适应当今迅速变化步伐的老旧系统和应用更加剧了这一问题。这种方式往往会导致企业发展停滞不前,且难以满足不断变化的业务需求。将现代化应用组合进行整合并转换到全新架构将能促进企业发展,能显著提高其敏捷性及创新能力。     

一类仿射非线性系统的概率密度函数形状控制

针对一类仿射非线性有界动态随机系统,提出一种最优概率密度函数（PDF）跟踪控制算法,使得系统的输出PDF跟踪给定的PDF．首先利用线性B样条解耦得到仿射非线性状态方程和PDF逼近方程,使PDF跟踪转化为状态方程输出权值的跟踪;然后采用线性时变序列逼近方法将非线性系统转化为线性时变系统,通过对线性系统的迭代运算得到非线性系统的最优跟踪控制器,从而实现最优PDF跟踪．理论分析和仿真实验均表明了所提出算法的有效性．     

具有D稳定性约束的T-S模糊系统H∞滤波器设计

研究一类离散T-S模糊系统的H_∞滤波问题.目的是设计一个滤波器使得滤波误差系统在给定的线性矩阵不等式（LMI）稳定范围内是二次稳定的,且满足指定的H_∞性能.经由模糊Lyapunov函数方法以及增加松弛变量,提出一个滤波器存在的充分条件,它体现为一组线性矩阵不等式（LMIs）可行解的形式.最后通过一个仿真例子验证了所提出的设计方法的有效性.     

被动多传感器自适应曲线模型跟踪新算法

针对被动多传感器机动目标跟踪系统中,由于目标机动性能的不确定以及存在的非线性而导致系统模型与目标实际运动模式难以匹配的问题,提出一种新的自适应曲线模型跟踪算法．该算法通过建立新的方向角模型,设计一种自适应的转弯角速度估计方法,实时计算每个采样时刻目标的切向加速度,以获得与目标实际运动模式相匹配的运动模型,并与扩展卡尔曼滤波相结合,有效提高了被动多传感器下机动目标的跟踪精度．     

高阶滑模微分器的分析与改进

首先采用Terminal吸引子函数代替原不连续函数,以避免滑模微分器的抖振;然后针对现有文献中高阶滑模微分器设计参数选取苛刻这一问题,放宽了设计参数选择范围,并分不同情况证明了高阶滑模微分器的稳定性．在此基础上,给出了高阶滑模微分器估计误差的上界与设计参数和滑模微分器阶次的关系式;通过对设计参数的选取和系统整体结构的分析,提出了高阶滑模微分器减小估计误差且更适于工程应用的设计方法,并给出所需条件．仿真结果表明了所得结论的正确性和有效性．