Data Mining - Concepts and Techniques.

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.

Graph Neural Networks: A Review of Methods and Applications

A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

XSEarch, a semantic search engine for XML, is presented. XSEarch has a simple query language, suitable for a naive user. It returns semantically related document fragments that satisfy the user's query. Query answers are ranked using extended information-retrieval techniques and are generated in an order similar to the ranking. Advanced indexing techniques were developed to facilitate efficient implementation of XSEarch. The performance of the different techniques as well as the recall and the precision were measured experimentally. These experiments indicate that XSEarch is efficient, scalable and ranks quality results highly.

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XSEarch: a semantic search engine for XML

Query processing that preserves both the data privacy of the owner and the query privacy of the client is a new research problem. It shows increasing importance as cloud computing drives more businesses to outsource their data and querying services. However, most existing studies, including those on data outsourcing, address the data privacy and query privacy separately and cannot be applied to this problem. In this paper, we propose a holistic and efficient solution that comprises a secure traversal framework and an encryption scheme based on privacy homomorphism. The framework is scalable to large datasets by leveraging an index-based approach. Based on this framework, we devise secure protocols for processing typical queries such as k-nearest-neighbor queries (kNN) on R-tree index. Moreover, several optimization techniques are presented to improve the efficiency of the query processing protocols. Our solution is verified by both theoretical analysis and performance study.

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Processing private queries over untrusted data cloud through privacy homomorphism

Galax is a light-weight, portable, open-source implementation of XQuery 1.0. Started in December 2000 as a small prototype designed to test the XQuery static type system, Galax has now become a solid implementation, aiming at full conformance with the family of XQuery 1.0 specifications. Because of its completeness and open architecture, Galax also turns out to be a very convenient platform for researchers interested in experimenting with XQuery optimization.

We demonstrate the Galax system as well as its most advanced features, including support for XPath 2.0, XML Schema and static type-checking. We also present some of our first experiments with optimization. Notably, we demonstrate query rewriting capabilities in the Galax compiler, and the ability to run queries on documents up to a Gigabyte without the need for preindexing. Although early versions of Galax have been shown in industrial conferences over the last two years, this is the first time it is demonstrated in the database community.

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Implementing XQuery 1.0: the Galax experience

DTDs have proved important in a variety of areas: transformations between XML and databases, XML storage, XML publishing, consistency analysis of XML specifications, typechecking, and optimization of XML queries. Much of this work depends on certain assumptions about DTDs, e.g., the absence of recursion and non-determinism. With this comes the need to justify these assumptions against DTDs in the real world. This paper surveys a number of DTDs collected from the Web, and provides statistics with respect to a variety of criteria commonly discussed in XML resesarch.

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What are real DTDs like

As the study of large graphs over hundreds of gigabytes becomes increasingly popular for various data-intensive applications in cloud computing, developing large graph processing systems has become a hot and fruitful research area. Many of those existing systems support a vertex-oriented execution model and allow users to develop custom logics on vertices. However, the inherently random access pattern on the vertex-oriented computation generates a significant amount of network traffic. While graph partitioning is known to be effective to reduce network traffic in graph processing, there is little attention given to how graph partitioning can be effectively integrated into large graph processing in the cloud environment. In this paper, we develop a novel graph partitioning framework to improve the network performance of graph partitioning itself, partitioned graph storage and vertex-oriented graph processing. All optimizations are specifically designed for the cloud network environment. In experiments, we develop a system prototype following Pregel (the latest vertex-oriented graph engine by Google), and extend it with our graph partitioning framework. We conduct the experiments with a real-world social network and synthetic graphs over 100GB each in a local cluster and on Amazon EC2. Our experimental results demonstrate the efficiency of our graph partitioning framework, and the effectiveness of network performance aware optimizations on the large graph processing engine.

Improving large graph processing on partitioned graphs in the cloud

When large XML documents published from a database are maintained externally, it is inefficient to repeatedly recompute them when the database is updated. Vastly preferable is incremental update, as common for views stored in a data warehouse. However, to support schema-directed publishing, there may be no simple query that defines the mapping from the database to the external document. To meet the need for efficient incremental update, this paper studies two approaches for incremental evaluation of ATGs [4], a formalism for schema-directed XML publishing. The reduction approach seeks to push as much work as possible to the underlying DBMS. It is based on a relational encoding of XML trees and a nontrivial translation of ATGs to SQL 99 queries with recursion. However, a weakness of this approach is that it relies on high-end DBMS features rather than the lowest common denominator. In contrast, the bud-cut approach pushes only simple queries to the DBNS and performs the bulk of the work in middleware. It capitalizes on the tree-structure of XML views to minimize unnecessary recomputations and leverages optimization techniques developed for XML publishing. While implementation of the reduction approach is not yet in the reach of commercial DBMS, we have implemented the bud-cut approach and experimentally evaluated its performance compared to recomputation.

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Byron Choi

Papers

Processing private queries over untrusted data cloud through privacy homomorphism

Implementing XQuery 1.0: the Galax experience

What are real DTDs like

Improving large graph processing on partitioned graphs in the cloud

Incremental evaluation of schema-directed XML publishing