Data Mining - Concepts and Techniques.

(1) Whereas the objectives of the Community, as laid down in the Treaty, as amended by the Treaty on European Union, include creating an ever closer union among the peoples of Europe, fostering closer relations between the States belonging to the Community, ensuring economic and social progress by common action to eliminate the barriers which divide Europe, encouraging the constant improvement of the living conditions of its peoples, preserving and strengthening peace and liberty and promoting democracy on the basis of the fundamental rights recognized in the constitution and laws of the Member States and in the European Convention for the Protection of Human Rights and Fundamental Freedoms;

Some Preliminary Comments on the DIRECTIVE 95/46/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data.

Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity/distance computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice. Inspired by the recent success of neural network approaches to several graph applications, such as node or graph classification, we propose a novel neural network based approach to address this classic yet challenging graph problem, aiming to alleviate the computational burden while preserving a good performance. The proposed approach, called SimGNN, combines two strategies. First, we design a learnable embedding function that maps every graph into an embedding vector, which provides a global summary of a graph. A novel attention mechanism is proposed to emphasize the important nodes with respect to a specific similarity metric. Second, we design a pairwise node comparison method to supplement the graph-level embeddings with fine-grained node-level information. Our model achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs. Taking GED computation as an example, experimental results on three real graph datasets demonstrate the effectiveness and efficiency of our approach. Specifically, our model achieves smaller error rate and great time reduction compared against a series of baselines, including several approximation algorithms on GED computation, and many existing graph neural network based models. Our study suggests SimGNN provides a new direction for future research on graph similarity computation and graph similarity search.

https://dl.acm.org/doi/pdf/10.1145/3289600.3290967

SimGNN: A Neural Network Approach to Fast Graph Similarity Computation

In this paper, we tackle the problem of constructing a differentially private synopsis for two-dimensional datasets such as geospatial datasets. The current state-of-the-art methods work by performing recursive binary partitioning of the data domains, and constructing a hierarchy of partitions. We show that the key challenge in partition-based synopsis methods lies in choosing the right partition granularity to balance the noise error and the non-uniformity error. We study the uniform-grid approach, which applies an equi-width grid of a certain size over the data domain and then issues independent count queries on the grid cells. This method has received no attention in the literature, probably due to the fact that no good method for choosing a grid size was known. Based on an analysis of the two kinds of errors, we propose a method for choosing the grid size. Experimental results validate our method, and show that this approach performs as well as, and often times better than, the state-of-the-art methods. We further introduce a novel adaptive-grid method. The adaptive grid method lays a coarse-grained grid over the dataset, and then further partitions each cell according to its noisy count. Both levels of partitions are then used in answering queries over the dataset. This method exploits the need to have finer granularity partitioning over dense regions and, at the same time, coarse partitioning over sparse regions. Through extensive experiments on real-world datasets, we show that this approach consistently and significantly outperforms the uniform-grid method and other state-of-the-art methods.

Differentially Private Grids for Geospatial Data

With the advent of cloud computing, individuals and organizations have become interested in moving their databases from local to remote cloud servers However, data owners and cloud service providers are not in the same trusted domain in practice For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective database utilization a very challenging task To address this challenge, in this paper, we propose L-EncDB, a novel lightweight encryption mechanism for database, which (i) keeps the database structure and (ii) supports efficient SQL-based queries To achieve this goal, a new format-preserving encryption (FPE) scheme is constructed in this paper, which can be used to encrypt all types of character strings stored in database Extensive analysis demonstrates that the proposed L-EncDB scheme is highly efficient and provably secure under existing security model

/pdf/l-encdb-a-lightweight-framework-for-privacy-preserving-data-4132lrgeob.pdf

L-EncDB: A lightweight framework for privacy-preserving data queries in cloud computing

Graphs are popular models for representing complex structure data and similarity search for graphs has become a fundamental research problem. Many techniques have been proposed to support similarity search based on the graph edit distance. However, they all suffer from certain drawbacks: high computational complexity, poor scalability in terms of database size, or not taking full advantage of indexes. To address these problems, in this paper, we propose SEGOS, an indexing and query processing framework for graph similarity search. First, an effective two-level index is constructed off-line based on sub-unit decomposition of graphs. Then, a novel search strategy based on the index is proposed. Two algorithms adapted from TA and CA methods are seamlessly integrated into the proposed strategy to enhance graph search. More specially, the proposed framework is easy to be pipelined to support continuous graph pruning. Extensive experiments are conducted on two real datasets to evaluate the effectiveness and scalability of our approaches.

/pdf/an-efficient-graph-indexing-method-3atbzs4o04.pdf

An Efficient Graph Indexing Method

Continuous monitoring of skylines over uncertain data streams

The skyline operator has received considerable attention from the database community, due to its importance in many applications including multicriteria decision making, preference answering, and so forth. In many applications where uncertain data are inherently exist, i.e., data collected from different sources in distributed locations are usually with imprecise measurements, and thus exhibit kind of uncertainty. Taking into account the network delay and economic cost associated with sharing and communicating large amounts of distributed data over an internet, an important problem in this scenario is to retrieve the global skyline tuples from all the distributed local sites with minimum communication cost. Based on the well-known notation of the probabilistic skyline query over centralized uncertain data, in this paper, we propose the notation of distributed skyline queries over uncertain data. Furthermore, two communication- and computation-efficient algorithms are proposed to retrieve the qualified skylines from distributed local sites. Extensive experiments have been conducted to verify the efficiency, the effectiveness and the progressiveness of our algorithms with both the synthetic and real data sets.

Efficient and Progressive Algorithms for Distributed Skyline Queries over Uncertain Data

In this paper, we address the problem of finding k-nearest neighbors (KNN) in sequence databases using the edit distance. Unlike most existing works using short and exact n-gram matchings together with a filter-and-refine framework for KNN sequence search, our new approach allows us to use longer but approximate n-gram matchings as a basis of KNN candidates pruning. Based on this new idea, we devise a pipeline framework over a two-level index for searching KNN in the sequence database. By coupling this framework together with several efficient filtering strategies, i.e. the frequency queue and the well-known Combined Algorithm (CA), our proposal brings various enticing advantages over existing works, including 1) huge reduction on false positive candidates to avoid large overheads on candidate verifications; 2) progressive result update and early termination; and 3) good extensibility to parallel computation. We conduct extensive experiments on three real datasets to verify the superiority of the proposed framework.

/pdf/efficient-and-effective-knn-sequence-search-with-approximate-sr8pkj1q00.pdf

Efficient and effective KNN sequence search with approximate n-grams

The skyline operator has received considerable attention from the database community, due to its importance in many applications including multi-criteria decision making, preference answering, and so forth. In many applications where uncertain data are inherently exist, i.e., data collected from different sources in distributed locations are usually with imprecise measurements, and thus exhibit kind of uncertainty. Taking into account the network delay and economic cost associated with sharing and communicating large amounts of distributed data over an internet, an important problem in this scenario is to retrieve the global skyline tuples from all the distributed local sites with minimum communication cost. Based on the well known notation of the probabilistic skyline query over centralized uncertain data, in this paper, for the first time, we propose the notation of distributed skyline queries over uncertain data. Furthermore, two communication-and computation-efficient algorithms are proposed to retrieve the qualified skylines from distributed local sites. Extensive experiments have been conducted to verify the efficiency and the effectiveness of our algorithms with both the synthetic and real data sets.

Xiaofeng Ding

Papers

An Efficient Graph Indexing Method

Continuous monitoring of skylines over uncertain data streams

Efficient and Progressive Algorithms for Distributed Skyline Queries over Uncertain Data

Efficient and effective KNN sequence search with approximate n-grams

Efficient and Progressive Algorithms for Distributed Skyline Queries over Uncertain Data