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检索条件"主题词=Streaming algorithm"

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Real-Time Top-R Topic Detection on Twitter with Topic Hijack Filtering 15

Real-Time Top-R Topic Detection on Twitter with Topic Hijack...

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21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Hayashi, Kohei Maehara, Takanori Toyoda, Masashi Kawarabayashi, Ken-ichi Natl Inst Informat Tokyo Japan JST ERATO Kawarabayashi Large Graph Project Kawaguchi Saitama Japan Shizuoka Univ Shizuoka 4228529 Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9781450336642

Twitter is a "what's-happening-right-now" tool that enables interested parties to follow thoughts and commentary of individual users in nearly real-time. While it is a valuable source of information for real-time topic detection and tracking, Twitter data are not clean because of noisy messages and users, which significantly diminish the reliability of obtained results. In this paper, we integrate both the extraction of meaningful topics and the filtering of messages over the Twitter stream. We develop a streaming algorithm for a sequence of document-frequency tables;our algorithm enables real-time monitoring of the top-10 topics from approximately 25% of all Twitter messages, while automatically filtering noisy and meaningless topics. We apply our proposed streaming algorithm to the Japanese Twitter stream and successfully demonstrate that, compared with other online nonnegative matrix factorization methods, our framework both tracks real world events with high accuracy in terms of the perplexity and simultaneously eliminates irrelevant topics.

关键词： Twitter topic detection streaming algorithm nonnegative matrix factorization noise filtering

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Efficient Incremental Computation of Aggregations over Sliding Windows 21

Efficient Incremental Computation of Aggregations over Slidi...

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27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Zhang, Chao Akbarinia, Reza Toumani, Farouk Univ Clermont Auvergne CNRS LIMOS Clermont Ferrand France Univ Montpellier INRIA LIRMM Montpellier France

ISBN: (纸本)9781450383325

Computing aggregation over sliding windows, i.e., finite subsets of an unbounded stream, is a core operation in streaming analytics. We propose PBA (Parallel Boundary Aggregator), a novel parallel algorithm that groups continuous slices of streaming values into chunks and exploits two buffers, cumulative slice aggregations and left cumulative slice aggregations, to compute sliding window aggregations efficiently. PBA runs in O(1) time, performing at most 3 merging operations per slide while consuming O(n) space for windows with n partial aggregations. Our empirical experiments demonstrate that PBA can improve throughput up to 4x while reducing latency, compared to state-of-the-art algorithms.

关键词： Data Stream streaming algorithm Sliding Window Aggregation

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SPOTHOT: Scalable Detection of Geo-spatial Events in Large Textual Streams 16

SPOTHOT: Scalable Detection of Geo-spatial Events in Large T...

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28th International Conference on Scientific and Statistical Database Management (SSDBM)

作者： Schubert, Erich Weiler, Michael Kriegel, Hans-Peter Ludwig Maximilians Univ Munchen Inst Informat Munich Germany

ISBN: (纸本)9781450342155

The analysis of social media data poses several challenges: first of all, the data sets are very large, secondly they change constantly, and third they are heterogeneous, consisting of text, images, geographic locations and social connections. In this article, we focus on detecting events consisting of text and location information, and introduce an analysis method that is scalable both with respect to volume and velocity. We also address the problems arising from differences in adoption of social media across cultures, languages, and countries in our event detection by efficient normalization. We introduce an algorithm capable of processing vast amounts of data using a scalable online approach based on the SigniTrend event detection system, which is able to identify unusual geo-textual patterns in the data stream without requiring the user to specify any constraints in advance, such as hashtags to track: In contrast to earlier work, we are able to monitor every word at every location with just a fixed amount of memory, compare the values to statistics from earlier data and immediately report significant deviations with minimal delay. Thus, this algorithm is capable of reporting "Breaking News" in real-time. Location is modeled using unsupervised geometric discretization and supervised administrative hierarchies, which permits detecting events at city, regional, and global levels at the same time. The usefulness of the approach is demonstrated using several real-world example use cases using Twitter data.

关键词： Local event detection bursty topic detection change detection online control charts time-series analysis anomaly detection trend detection geo-social media rich geospatial data scalable real-time data analysis streaming algorithm

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BeauCoup: Answering Many Network Traffic Queries, One Memory Update at a Time 20

BeauCoup: Answering Many Network Traffic Queries, One Memory...

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Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication

作者： Chen, Xiaoqi Landau-Feibish, Shir Braverman, Mark Rexford, Jennifer Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781450379557

Network administrators constantly monitor network traffic for congestion and attacks. They need to perform a large number of measurements on the traffic simultaneously, to detect different types of anomalies such as heavy hitters or super-spreaders. Existing techniques often focus on a single statistic (e.g., traffic volume) or traffic attribute (e.g., destination IP). However, performing numerous heterogeneous measurements within the constrained memory architecture of modern network devices poses significant challenges, due to the limited number of memory accesses allowed per packet. We propose BeauCoup, a system based on the coupon collector problem, that supports multiple distinct counting queries simultaneously while making only a small constant number of memory accesses per packet. We implement BeauCoup on PISA commodity programmable switches, satisfying the strict memory size and access constraints while using a moderate portion of other data-plane hardware resources. Evaluations show BeauCoup achieves the same accuracy as other sketch-based or sampling-based solutions using 4x fewer memory access.

关键词： streaming algorithm Sketching Distinct Counting Data Plane Programmable Switch Network Measurement

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Finding frequent items in data streams

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29th International Colloquium on Automata, Languages and Programming

作者： Charikar, M Chen, K Farach-Colton, M Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Univ Calif Berkeley Div Comp Sci Berkeley CA 94720 USA Rutgers State Univ Dept Comp Sci Piscataway NJ 08855 USA

ISBN: (纸本)3540438645

We present a I-pass algorithm for estimating the most frequent items in a data stream using limited storage space. Our method relies on a data structure called a COUNT SKETCH, which allows us to reliably estimate the frequencies of frequent items in the stream. Our algorithm achieves better space bounds than the previously known best algorithms for this problem for several natural distributions on the item frequencies. In addition, our algorithm leads directly to a 2-pass algorithm for the problem of estimating the items with the largest (absolute) change in frequency between two data streams. To our knowledge, this latter problem has not been previously studied in the literature. (C) 2003 Elsevier B.V. All rights reserved.

关键词： frequent items streaming algorithm approximation

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SUBMODULAR MAXIMIZATION WITH MULTI-KNAPSACK CONSTRAINTS AND ITS APPLICATIONS IN SCIENTIFIC LITERATURE RECOMMENDATIONS

SUBMODULAR MAXIMIZATION WITH MULTI-KNAPSACK CONSTRAINTS AND ...

引用

IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Yu, Qilian Xu, Easton Li Cui, Shuguang Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9781509045457

Submodular maximization problems belong to the family of combinatorial optimization problems and enjoy wide applications. In this paper, we focus on the problem of maximizing a monotone submodular function subject to a d-knapsack constraint, for which we propose a streaming algorithm that achieves a (1/1+2d - epsilon)-approximation of the optimal value, while it only needs one single pass through the dataset without storing all the data in the memory. In our experiments, we extensively evaluate the effectiveness of our proposed algorithm via an application in scientific literature recommendation. It is observed that the proposed streaming algorithm achieves both execution speedup and memory saving by several orders of magnitude, compared with existing approaches.

关键词： Submodular Optimization streaming algorithm Scientific Literature Recommendation

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Assignment problems and their application in economics

Assignment problems and their application in economics

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作者： Melika Abolhassani University of Maryland

学位级别：博士

Four assignment problems are introduced in this thesis, and they are approached based on the context they are presented in. The underlying graphs of the assignment problems in this thesis are in most cases bipartite graphs with two sets of vertices corresponding to the agents and the resources. An edge might show the interest of an agent in a resource or willingness of a manufacturer to produce the corresponding product of a market, to name a few examples. The rst problem studied in this thesis is a two-stage stochastic matching problem in both online and oine versions. In this work, which is presented in Chapter 2 of this thesis, a coordinator tries to benet by having access to the statistics of the future price discounts which can be completely unpredictable for individual customers. In our model, individual risk-averse customers want to book hotel rooms for their future vacation; however, they are unwilling to leave booking to the last minute which might result in huge savings for them since they have to take the risk of all the hotel rooms being sold out. Instead of taking this risk, individual customers make contracts with a coordinator who can spread the risk over many such cases and also has more information on the probability distribution of the future prices. In the rst stage, the coordinator agrees to serve some buyers, and then in the second stage, once the nal prices have been revealed, he books rooms for them just as he promised. An agreement between the coordinator and each buyer consists of a set of acceptable hotels for the customer and a single price. Two models for this problem are investigated. In the rst model, the details of the agreements are proposed by the buyer, and we propose a bicriteria-style approximation algorithm that gives a constant-factor approximation to the objective function by allowing a bounded fraction of our hotel bookings to overlap. In the second model, the details of the agreements are proposed by the coordinator, and we s

关键词： approximation algorithm bipartite graph cournot competition equilibria Online Matching streaming algorithm

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Heavy-hitter detection using a hardware sketch with the Countmin-CU algorithm 21

Heavy-hitter detection using a hardware sketch with the Coun...

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21st Euromicro Conference on Digital System Design (DSD)

作者： Saavedra, Antonio Hernandez, Cecilia Figueroa, Miguel Univ Concepcion Dept Elect Engn Concepcion Chile Univ Concepcion Dept Comp Sci Concepcion Chile Ctr Biotechnol & Bioengn CeBiB Santiago Chile

ISBN: (纸本)9781538673768

We present a custom hardware architecture for fast heavy hitter detection in large data streams. The architecture probabilistically estimates the frequency of each element in the data stream using the Countmin-CU sketch with the H3 family of hash functions. The sketch is stored in on-chip memory, and the architecture exploits the parallelism available in the data by simultaneously processing each row of the sketch. The hash functions map each element to a set of counters on the sketch, and the sketch increments the counters that hold the minimum value, which corresponds to the estimated frequency of the element. The hash functions and sorting network are implemented in hardware as fully pipelined circuits, in order to maximize their operating clock frequency. We show a prototype of the architecture running on a Xilinx Kintex-7 XC7K325T FPGA operating with a 300MHz clock, which can process a stream of 3,982,496 32-bit elements and detect the heavy hitters with an 4x16,384-element sketch in 13.27ms, achieving a speedup of 768 compared to a modern desktop computer.

关键词： Heavy hitter streaming algorithm hardware acceleration Field-Programmable Gate Array

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FCM-Sketch: Generic Network Measurements with Data Plane Support 20

FCM-Sketch: Generic Network Measurements with Data Plane Sup...

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16th International Conference on Emerging Networking Experiments and Technologies

作者： Song, Cha Hwan Kannan, Pravein Govindan Low, Bryan Kian Hsiang Chan, Mun Choon Natl Univ Singapore Sch Comp Singapore Singapore

ISBN: (纸本)9781450379489

Sketches have successfully provided accurate and fine-grained measurements (e.g., flow size and heavy hitters) which are imperative for network management. In particular, Count-Min (CM) sketch is widely utilized in many applications due to its simple design and ease of implementation. There have been many efforts to build monitoring frameworks based on Count-Min sketch. However, these frameworks either support very specific measurement tasks or they cannot be implemented on high-speed programmable hardware (PISA). In this work, we propose FCM, a framework that is designed to support generic network measurement with high accuracy. Our key contribution is FCM-Sketch, a data structure that has a lightweight implementation on the emerging PISA programmable switches. FCM-Sketch can also be used as a substitute for CM-Sketch in applications that use CM-Sketch. We have implemented FCM-Sketch on a commodity programmable switch (Barefoot Tofino) using the P4 language. Our evaluation shows that FCM-Sketch can reduce the errors in many measurement tasks by 50% to 80% compared to CM-Sketch and other state-of-the-art approaches.

关键词： streaming algorithm Sketches Network Measurement Generic Data Plane Programmable Switch PISA P4

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streaming Influence Maximization in Social Networks Based on Multi-Action Credit Distribution

Streaming Influence Maximization in Social Networks Based on...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Qilian Yu Hang Li Yun Liao Shuguang Cui Dept. of ECE University of California Davis CA 95616 USA Dept. of ECE University of California San Diego CA 92093 USA

ISBN: (纸本)9781538646595

In a social network, influence maximization is the problem of identifying a set of users that own the maximum influence ability across the network. In this paper, a novel credit distribution (CD) based model, termed as the multi-action CD (mCD) model, is introduced to quantify the influence ability of each user. Compared to existing models, the new model can work with practical datasets where one type of action is recorded for multiple times. Based on this model, influence maximization is formulated as a submodular maximization problem under a knapsack constraint, which is NP-hard. An efficient streaming algorithm is developed to achieve ([1/3]-ε) approximation of the optimality. Experiments conducted on real Twitter dataset demonstrate that the mCD model enjoys high accuracy compared to the conventional CD model in estimating the total number of people who get influenced in a social network. Furthermore, compared to the greedy algorithm, the proposed single-pass streaming algorithm achieves similar performance in terms of influence maximization, while running several orders of magnitude faster.

关键词： Social Networks Influence Maximization Credit Distribution Submodularity streaming algorithm

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