24206437
Chopper: Efficient algorithm for tree mining
With the development of Internet, frequent pattern mining has been extended to more complex patterns like tree mining and graph mining. Such applications arise in complex domains like bioinformatics, web mining, etc. In this paper, we present a novel algorithm, namedChopper, to discover frequent subtrees from ordered labeled trees. An extensive performance study shows that the newly developed algorithm outperformsTreeMiner V, one of the fastest methods proposed previously, in mining large databases. At the end of this paper, the potential improvement ofChopper is mentioned.
30881426
Constraint-Based Graph Mining in Large Database
Currently, constraints are increasingly considered as a kind of means of user- or expert-control for filtering those unsatisfied and redundant patterns rapidly during the web mining process. Recent work has highlighted the importance of constraint-based mining paradigm in the context of frequent itemsets, sequences, and many other interesting patterns in large database. However, it is still not clear how to push various constraints systematically into graph mining process. In this paper, we categorize various graph-based constraints into several major classes and develop a framework CabGin (i.e. C onstr a int- b ased G raph M in ing) to push them into mining process by their categories. Non-monotonic aggregates like average also can be pushed into CabGin with minor revision. Experimental results show that CabGin can prunes a large search space effectively by pushing graph-based constraints into mining process.
34121335
Data-Aware Clustering Hierarchy for Wireless Sensor Networks
In recent years, the wireless sensor network (WSN) is employed a wide range of applications. But existing communication protocols for WSN ignore the characteristics of collected data and set routes only according to the mutual distance and residual energy of sensors. In this paper we propose a Data-Aware Clustering Hierarchy (DACH), which organizes the sensors based on both distance information and data distribution in the network Furthermore, we also present a multi-granularity query processing method based on DACH, which can estimate the query result more efficiently. Our empirical study shows that DACH has higher energy efficiency than Low-Energy Adaptive Clustering Hierarchy (LEACH), and the multi-granularity query processing method based on DACH brings more accurate results than a random access system using same cost of energy.
14792668
Personalization as a service: the architecture and a case study
Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.
8249361
SUDEPHIC: Self-Tuning Density-Based Partitioning and Hierarchical Clustering
Clustering is one of the primary techniques in data mining, for which to find the user expecting result is a major issue. However, to dynamically specify the parameters for clustering algorithms presents an obstacle for users. This paper firstly introduces a novel density-based partitioning and hierarchical algorithm, which makes it easy to employ synthetic feedback mechanism in clustering. Additionally, by investigating into the relation between parameters and the clustering result, we propose a self-tuning technique for the setting of parameters. Meanwhile, the density distribution within a cluster can be expressed in the result for the user to specify the cluster’s feature. The algorithm is both evaluated in theory and practice. It outperforms many existing algorithms both in efficiency and quality.
10409272
Repairing Inconsistent XML Documents
XML document may contain inconsistencies that violate predefined integrity constraints, and there are two basic concepts for this problem: Repair is the data consistent with the integrity constraints, and also minimally differs from the original one. Consistent data is the data common for every possible repair. In this paper, first we give a general constraint model for XML, which can express functional dependencies, keys and multivalued dependencies. Next we provide a repair framework for inconsistent XML document with three basic update operations: node insertion, node deletion and value modification. Following this approach, we introduce the concept of repair for inconsistent XML document, discuss the chase process to generate repairs, and prove some important properties of the chase process. Finally we give a method to obtain the greatest lower bound of all possible repairs, which is sufficient for consistent data.
697583
Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows
MOTIVATION
Typical high-throughput genotyping techniques produce numerous missing calls that confound subsequent analyses, such as disease association studies. Common remedies for this problem include removing affected markers and/or samples or, otherwise, imputing the missing data. On small marker sets imputation is frequently based on a vote of the K-nearest-neighbor (KNN) haplotypes, but this technique is neither practical nor justifiable for large datasets.


RESULTS
We describe a data structure that supports efficient KNN queries over arbitrarily sized, sliding haplotype windows, and evaluate its use for genotype imputation. The performance of our method enables exhaustive exploration over all window sizes and known sites in large (150K, 8.3M) SNP panels. We also compare the accuracy and performance of our methods with competing imputation approaches.


AVAILABILITY
A free open source software package, NPUTE, is available at http://compgen.unc.edu/software, for non-commercial uses.
38794991
Localized Recursive Estimation in Wireless Sensor Networks
This paper proposes a localized recursive estimation scheme for parameter estimation in wireless sensor networks. Given any parameter occurred at some location and time, a number of sensors recursively estimates the parameter by using their local measurements of the parameter that is attenuated with the distance between a sensor and the target location and corrupted by noises. Compared with centralized estimation schemes that transmit all measurements to a sink (or a fusion center), the recursive scheme needs only to transmit the final estimate to a sink. When a sink is faraway from the sensors and multihop communications have to be used, using localized recursive estimation can help to reduce energy consumption and reduce network traffic load. Furthermore, the most efficient sequence of sensors for estimation is defined and the necessary condition for such a sequence is determined. Some numerical examples are also provided. By using some typical industrial sensor parameter values, it is shown that recursive scheme consumes much less energy when the sink is three hops or more faraway from the local sensors.
573449
Recent progress on selected topics in database research — A report by nine young Chinese researchers working in the United States
The study on database technologies, or more generally, the technologies of data and information management, is an important and active research field. Recently, many exciting results have been reported. In this fast growing field, Chinese researchers play more and more active roles. Research papers from Chinese scholars, both in China and abroad, appear in prestigious academic forums.In this paper, we, nine young Chinese researchers working in the United States, present concise surveys and report our recent progress on the selected fields that we are working on. Although the paper covers only a small number of topics and the selection of the topics is far from balanced, we hope that such an effort would attract more and more researchers, especially those in China, to enter the frontiers of database research and promote collaborations. For the obvious reason, the authors are listed alphabetically, while the sections are arranged in the order of the author list.
2673667
ApproxMAP: Approximate Mining of Consensus Sequential Patterns
Conventional sequential pattern mining methods may meet inherent difficulties in mining databases with long sequences and noise. They may generate a huge number of short and trivial patterns but fail to find interesting patterns approximately shared by many sequences. In this paper, we propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. We present an efficient and effective algorithm, ApproxMAP, to mine consensus patterns from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We use a real case study to illustrate the effectiveness of ApproxMAP.
2987996
Mining patterns in long sequential data with noise
Pattern discovery in time series data has been a problem of great importance in many fields, e.g., computational biology, performance analysis, consumer behavior, etc. Recently, considerable amount of research has been carried out in this area. The facts that the input data is typically very large and noises may present in various formats pose great challenge to the mining process. Recently, we have made several new research advances in this area. In this paper, we present some of them. We will survey new models proposed to address different types of noises as well as scalable algorithms developed for efficiently mining patterns under each model.