arXiv subjects

Jianzhong Li

Publications and source records attributed to Jianzhong Li.

50 records · Page 3Linked to original sources

arXiv2018

MISS: Finding Optimal Sample Sizes for Approximate Analytics

Nowadays, sampling-based Approximate Query Processing (AQP) is widely regarded as a promising way to achieve interactivity in big data analytics. To build such an AQP system, finding the minimal sample size for a query regarding given error constraints in general, called Sample Size Optimization (SSO), is an essential yet unsolved problem. Ideally, the goal of solving the SSO problem is to achieve statistical accuracy, computational efficiency and broad applicability all at the same time. Existing approaches either make idealistic assumptions on the statistical properties of the query, or completely disregard them. This may result in overemphasizing only one of the three goals while neglect the others. To overcome these limitations, we first examine carefully the statistical properties shared by common analytical queries. Then, based on the properties, we propose a linear model describing the relationship between sample sizes and the approximation errors of a query, which is called the error model. Then, we propose a Model-guided Iterative Sample Selection (MISS) framework to solve the SSO problem generally. Afterwards, based on the MISS framework, we propose a concrete algorithm, called $L^2$Miss, to find optimal sample sizes under the $L^2$ norm error metric. Moreover, we extend the $L^2$Miss algorithm to handle other error metrics. Finally, we show theoretically and empirically that the $L^2$Miss algorithm and its extensions achieve satisfactory accuracy and efficiency for a considerably wide range of analytical queries.

cs.DB↗

arXiv2018

QuickIM: Efficient, Accurate and Robust Influence Maximization Algorithm on Billion-Scale Networks

The Influence Maximization (IM) problem aims at finding k seed vertices in a network, starting from which influence can be spread in the network to the maximum extent. In this paper, we propose QuickIM, the first versatile IM algorithm that attains all the desirable properties of a practically applicable IM algorithm at the same time, namely high time efficiency, good result quality, low memory footprint, and high robustness. On real-world social networks, QuickIM achieves the $Ω(n + m)$ lower bound on time complexity and $Ω(n)$ space complexity, where $n$ and $m$ are the number of vertices and edges in the network, respectively. Our experimental evaluation verifies the superiority of QuickIM. Firstly, QuickIM runs 1-3 orders of magnitude faster than the state-of-the-art IM algorithms. Secondly, except EasyIM, QuickIM requires 1-2 orders of magnitude less memory than the state-of-the-art algorithms. Thirdly, QuickIM always produces as good quality results as the state-of-the-art algorithms. Lastly, the time and the memory performance of QuickIM is independent of influence probabilities. On the largest network used in the experiments that contains more than 3.6 billion edges, QuickIM is able to find hundreds of influential seeds in less than 4 minutes, while all the state-of-the-art algorithms fail to terminate in an hour.

cs.SI↗

arXiv2018

Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment

Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission and processing power. Encoding the image information in quantum-mechanical systems instead of classical ones and replacing classical with quantum information processing may alleviate some of these challenges. By encoding and processing the image information in quantum-mechanical systems, we here demonstrate the framework of quantum image processing, where a pure quantum state encodes the image information: we encode the pixel values in the probability amplitudes and the pixel positions in the computational basis states. Our quantum image representation reduces the required number of qubits compared to existing implementations, and we present image processing algorithms that provide exponential speed-up over their classical counterparts. For the commonly used task of detecting the edge of an image, we propose and implement a quantum algorithm that completes the task with only one single-qubit operation, independent of the size of the image. This demonstrates the potential of quantum image processing for highly efficient image and video processing in the big data era.

quant-ph↗

arXiv2017

Optimal Scheduling of Friendly Jammers for Securing Wireless Communication

Wireless communication systems, such as wireless sensor networks and RFIDs, are increasingly adopted to transfer potential highly sensitive information. Since the wireless medium has a sharing nature, adversaries have a chance to eavesdrop confidential information from the communication systems. Adding artificial noises caused by friendly jammers emerges as a feasible defensive technique against adversaries. This paper studies the schedule strategies of friendly jammers, which are randomly and redundantly deployed in a circumscribed geographical area and can be unrechargeable or rechargeable, to maximize the lifetime of the jammer networks and prevent the cracking of jamming effect made by the eavesdroppers, under the constraints of geographical area, energy consumption, transmission power, and threshold level. An approximation algorithm as baseline is first proposed using the integer linear programming model. To further reduce the computational complexity, a heuristic algorithm based on the greedy strategy that less consumption leads to longer lifetime is also proposed. Finally, extensive simulation results show that the proposed algorithms are effective and efficient.

cs.CR↗

arXiv2017

Discovery of Paradigm Dependencies

Missing and incorrect values often cause serious consequences. To deal with these data quality problems, a class of common employed tools are dependency rules, such as Functional Dependencies (FDs), Conditional Functional Dependencies (CFDs) and Edition Rules (ERs), etc. The stronger expressing ability a dependency has, data with the better quality can be obtained. To the best of our knowledge, all previous dependencies treat each attribute value as a non-splittable whole. Actually however, in many applications, part of a value may contains meaningful information, indicating that more powerful dependency rules to handle data quality problems are possible. In this paper, we consider of discovering such type of dependencies in which the left hand side is part of a regular-expression-like paradigm, named Paradigm Dependencies (PDs). PDs tell that if a string matches the paradigm, element at the specified position can decides a certain other attribute's value. We propose a framework in which strings with similar coding rules and different lengths are clustered together and aligned vertically, from which PDs can be discovered directly. The aligning problem is the key component of this framework and is proved in NP-Complete. A greedy algorithm is introduced in which the clustering and aligning tasks can be accomplished simultaneously. Because of the greedy algorithm's high time complexity, several pruning strategies are proposed to reduce the running time. In the experimental study, three real datasets as well as several synthetical datasets are employed to verify our methods' effectiveness and efficiency.

cs.DB↗

arXiv2017

Diversified Coherent Core Search on Multi-Layer Graphs

Mining dense subgraphs on multi-layer graphs is an interesting problem, which has witnessed lots of applications in practice. To overcome the limitations of the quasi-clique-based approach, we propose d-coherent core (d-CC), a new notion of dense subgraph on multi-layer graphs, which has several elegant properties. We formalize the diversified coherent core search (DCCS) problem, which finds k d-CCs that can cover the largest number of vertices. We propose a greedy algorithm with an approximation ratio of 1 - 1/e and two search algorithms with an approximation ratio of 1/4. The experiments verify that the search algorithms are faster than the greedy algorithm and produce comparably good results as the greedy algorithm in practice. As opposed to the quasi-clique-based approach, our DCCS algorithms can fast detect larger dense subgraphs that cover most of the quasi-clique-based results.

cs.DB↗

arXiv2016

Efficient Entity Resolution on Heterogeneous Records

Entity resolution (ER) is the problem of identifying and merging records that refer to the same real-world entity. In many scenarios, raw records are stored under heterogeneous environment. Specifically, the schemas of records may differ from each other. To leverage such records better, most existing work assume that schema matching and data exchange have been done to convert records under different schemas to those under a predefined schema. However, we observe that schema matching would lose information in some cases, which could be useful or even crucial to ER. To leverage sufficient information from heterogeneous sources, in this paper, we address several challenges of ER on heterogeneous records and show that none of existing similarity metrics or their transformations could be applied to find similar records under heterogeneous settings. Motivated by this, we design the similarity function and propose a novel framework to iteratively find records which refer to the same entity. Regarding efficiency, we build an index to generate candidates and accelerate similarity computation. Evaluations on real-world datasets show the effectiveness and efficiency of our methods.

cs.DB↗

arXiv2016

Data Source Selection for Information Integration in Big Data Era

In Big data era, information integration often requires abundant data extracted from massive data sources. Due to a large number of data sources, data source selection plays a crucial role in information integration, since it is costly and even impossible to access all data sources. Data Source selection should consider both efficiency and effectiveness issues. For efficiency, the approach should achieve high performance and be scalability to fit large data source amount. From effectiveness aspect, data quality and overlapping of sources are to be considered, since data quality varies much from data sources, with significant differences in the accuracy and coverage of the data provided, and the overlapping of sources can even lower the quality of data integrated from selected data sources. In this paper, we study source selection problem in \textit{Big Data Era} and propose methods which can scale to datasets with up to millions of data sources and guarantee the quality of results. Motivated by this, we propose a new object function taking the expected number of true values a source can provide as a criteria to evaluate the contribution of a data source. Based on our proposed index we present a scalable algorithm and two pruning strategies to improve the efficiency without sacrificing precision. Experimental results on both real world and synthetic data sets show that our methods can select sources providing a large proportion of true values efficiently and can scale to massive data sources.

cs.DB↗

arXiv2016

Technique Report: Scheduling Flows with Multiple Service Frequency Constraints

With the fast development of wireless technologies, wireless applications have invaded various areas in people's lives with a wide range of capabilities. Guaranteeing Quality-of-Service (QoS) is the key to the success of those applications. One of the QoS requirements, service frequency, is very important for tasks including multimedia transmission in the Internet of Things. A service frequency constraint denotes the length of time period during which a link can transmit at least once. Unfortunately, it has not been well addressed yet. Therefore, this paper proposes a new framework to schedule multi transmitting flows in wireless networks considering service frequency constraint for each link. In our model, the constraints for flows are heterogeneous due to the diversity of users' behaviors. We first introduce a new definition for network stability with service frequency constraints and demonstrate that the novel scheduling policy is throughput-optimal in one fundamental category of network models. After that, we discuss the performance of a wireless network with service frequency constraints from the views of capacity region and total queue length. Finally, a series of evaluations indicate the proposed scheduling policy can guarantee service frequency and achieve a good performance on the aspect of queue length of each flow.

cs.NI↗

arXiv2015

SimRank Computation on Uncertain Graphs

SimRank is a similarity measure between vertices in a graph, which has become a fundamental technique in graph analytics. Recently, many algorithms have been proposed for efficient evaluation of SimRank similarities. However, the existing SimRank computation algorithms either overlook uncertainty in graph structures or is based on an unreasonable assumption (Du et al). In this paper, we study SimRank similarities on uncertain graphs based on the possible world model of uncertain graphs. Following the random-walk-based formulation of SimRank on deterministic graphs and the possible worlds model of uncertain graphs, we define random walks on uncertain graphs for the first time and show that our definition of random walks satisfies Markov's property. We formulate the SimRank measure based on random walks on uncertain graphs. We discover a critical difference between random walks on uncertain graphs and random walks on deterministic graphs, which makes all existing SimRank computation algorithms on deterministic graphs inapplicable to uncertain graphs. To efficiently compute SimRank similarities, we propose three algorithms, namely the baseline algorithm with high accuracy, the sampling algorithm with high efficiency, and the two-phase algorithm with comparable efficiency as the sampling algorithm and about an order of magnitude smaller relative error than the sampling algorithm. The extensive experiments and case studies verify the effectiveness of our SimRank measure and the efficiency of our SimRank computation algorithms.

cs.DB↗

arXiv2015

Efficient Influence Maximization in Weighted Independent Cascade Model

Influence maximization(IM) problem is to find a seed set in a social network which achieves the maximal influence spread. This problem plays an important role in viral marketing. Numerous models have been proposed to solve this problem. However, none of them considers the attributes of nodes. Paying all attention to the structure of network causes some trouble applying these models to real-word applications. Motivated by this, we present weighted independent cascade (WIC) model, a novel cascade model which extends the applicability of independent cascade(IC) model by attaching attributes to the nodes. The IM problem in WIC model is to maximize the value of nodes which are influenced. This problem is NP-hard. To solve this problem, we present a basic greedy algorithm and Weight Reset(WR) algorithm. Moreover, we propose Bounded Weight Reset(BWR) algorithm to make further effort to improve the efficiency by bounding the diffusion node influence. We prove that BWR is a fully polynomial-time approximation scheme(FPTAS). Experimentally, we show that with additional node attribute, the solution achieved by WIC model outperforms that of IC model in nearly 90%. The experimental results show that BWR can achieve excellent approximation and faster than greedy algorithm more than three orders of magnitude with little sacrifice of accuracy. Especially, BWR can handle large networks with millions of nodes in several tens of seconds while keeping rather high accuracy. Such result demonstrates that BWR can solve IM problem effectively and efficiently.

cs.SI↗

arXiv2013

Parallelization in Extracting Fresh Information from Online Social Network

Online Social Network (OSN) is one of the most hottest services in the past years. It preserves the life of users and provides great potential for journalists, sociologists and business analysts. Crawling data from social network is a basic step for social network information analysis and processing. As the network becomes huge and information on the network updates faster than web pages, crawling is more difficult because of the limitations of band-width, politeness etiquette and computation power. To extract fresh information from social network efficiently and effectively, this paper presents a novel crawling method and discusses parallelization architecture of social network. To discover the feature of social network, we gather data from real social network, analyze them and build a model to describe the discipline of users' behavior. With the modeled behavior, we propose methods to predict users' behavior. According to the prediction, we schedule our crawler more reasonably and extract more fresh information with parallelization technologies. Experimental results demonstrate that our strategies could obtain information from OSN efficiently and effectively.

cs.SI↗

arXiv2013

Harbinger: An Analyzing and Predicting System for Online Social Network Users' Behavior

Online Social Network (OSN) is one of the hottest innovations in the past years, and the active users are more than a billion. For OSN, users' behavior is one of the important factors to study. This demonstration proposal presents Harbinger, an analyzing and predicting system for OSN users' behavior. In Harbinger, we focus on tweets' timestamps (when users post or share messages), visualize users' post behavior as well as message retweet number and build adjustable models to predict users' behavior. Predictions of users' behavior can be performed with the discovered behavior models and the results can be applied to many applications such as tweet crawler and advertisement.

cs.SI↗

arXiv2012

Efficient Subgraph Matching on Billion Node Graphs

The ability to handle large scale graph data is crucial to an increasing number of applications. Much work has been dedicated to supporting basic graph operations such as subgraph matching, reachability, regular expression matching, etc. In many cases, graph indices are employed to speed up query processing. Typically, most indices require either super-linear indexing time or super-linear indexing space. Unfortunately, for very large graphs, super-linear approaches are almost always infeasible. In this paper, we study the problem of subgraph matching on billion-node graphs. We present a novel algorithm that supports efficient subgraph matching for graphs deployed on a distributed memory store. Instead of relying on super-linear indices, we use efficient graph exploration and massive parallel computing for query processing. Our experimental results demonstrate the feasibility of performing subgraph matching on web-scale graph data.

cs.DB↗