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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhong, Kai Sun, Luming Ji, Tao Li, Cuiping Chen, Hong Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Shanghai Yunxi Technology Co. Ltd. China Engineering Research Center of Database and Business Intelligence MOE China

ISBN: (纸本)9798350317152

Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel framework for query optimization based on deep reinforcement learning. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns from these experiences to improve its optimization capability. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance. Compared to PostgreSQL, FOSS achieves speedup ranging from 1.15x to 8.33x in total latency across different benchmarks. © 2024 IEEE.

关键词： Reinforcement learning

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A frequency and two-hop configuration checking-driven local search algorithm for the minimum weakly connected dominating set problem

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Neural Computing and Applications 2024年第22期36卷 13833-13852页

作者： Li, Ruizhi He, Jintao Lin, Cuisong Liu, Ying Hu, Shuli Yin, Minghao Jilin University of Finance and Economics Changchun130117 China Northeast Normal University Changchun130117 China Business Big Data Research Center of Jilin Province Changchun130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

The minimum weakly connected dominating set problem is a typical NP-hard problem with a wide range of applications. To solve this problem, we propose a frequency property and two-hop configuration checking strategy-driven local search algorithm (FCC2LS). In this algorithm, we first propose a lock-vertex-based initial solution construction procedure. This procedure guarantees that certain vertices, which must be included in the optimal solution, are added to the solution. Second, we propose a two-hop configuration checking strategy and a frequency property. The two-hop configuration checking strategy is a new variant of the original configuration checking strategy, which prohibits vertices with unchanged configuration from being added to the candidate solution. The frequency property is used to record the number of times for each vertex is added to the solution, which can increase the diversity of selected vertices. Third, we combine two scoring functions, Dscore and Nscore, with the above strategies and propose effective vertex selection methods to help the algorithm select suitable vertices to add into or remove from the candidate solutions. Finally, we evaluate the proposed algorithm FCC2LS with four state-of-the-art algorithms on four groups of benchmark instances. Experimental results show that our algorithm performs better on four classical benchmark instances. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Combinatorial optimization

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Logsonomy - Social information retrieval with logdata 08

Logsonomy - Social information retrieval with logdata

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HYPERTEXT'08: 19th ACM Conference on Hypertext and Hypermedia 2008, HT'08

作者： Krause, Beate Hotho, Andreas Jäschke, Robert Stumme, Gerd Knowledge and Data Engineering Group University of Kassel Wilhelmshoher Allee 73 34121 Kassel Germany Research Center L3S Appelstr. 9a 30167 Hannover Germany

ISBN: (纸本)9781595939852

Social bookmarking systems constitute an established part of the Web 2,0. in such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonornies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today's search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonornies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a, large snapshot of *** and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics. Copyright 2008 ACM.

关键词： Search engines

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On making data warehouses active

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2000年 1874卷 34-46页

作者： Schre, Michael Thalhammer, Thomas University of South Australia Advanced Computing Research Center Australia University of Linz Data and Knowledge Engineering Austria

ISBN: (纸本)3540679804

data warehouses, which are the core elements of On-Line Analytical Processing (OLAP) systems, are passive since all tasks related to analyzing and making decisions must be carried out manually. This paper introduces a novel architecture, the active data warehouse, which applies the idea of event-condition-action rules (ECA rules) from active database systems to automize repetitive analysis and decision tasks in data warehouses. The work of an analyst is mimicked by analysis rules, which extend the capabilities of conventional ECA rules in order to support multidimensional analysis and decision making. © Springer-Verlag Berlin Heidelberg 2000.

关键词： data warehouses

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Document clustering in antimicrobial peptides research

Document clustering in antimicrobial peptides research

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11th Americas Conference on Information Systems, AMCIS 2005

作者： Peng, Yi Yan, Nian Kou, Gang Chen, Zhengxin Shi, Yong Peter Kiewit Institute of Information Science Technology and Engineering University of Nebraska Omaha NE 68182 United States Chinese Academy of Sciences Research Center Data Technology and Knowledge Economy Beijing 100039 China

ISBN: (纸本)9781604235531

Antimicrobial peptides are small peptides encoded by genes. The research area of antimicrobial peptides has attracted intense attention in recent years because "their potential use in the cure of infectious diseases caused by pathogens that have become counteractive to traditional antibiotics" (Boman 1994). There exist huge amount of antimicrobial peptides research articles and this number is continuously increasing. Although some biomedical databases, such as PubMed, have been well established, they provide only query-based information retrieval and end-users need to manually find out relevant information from thousands of retrieved articles. The objective of this paper is to apply one of the text mining techniques, document clustering, which groups similar documents into clusters, to text documents collected from PubMed using keyword "antimicrobial peptides". The results of our work can help researchers to discover meaningful groups of antimicrobial peptides articles in an efficient manner.

关键词： Information retrieval

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Using optimization-based classification method for massive datasets

Using optimization-based classification method for massive d...

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11th Americas Conference on Information Systems, AMCIS 2005

作者： Peng, Yi Shi, Yong Kou, Gang Chen, Zhengxin Peter Kiewit Institute of Information Science Technology and Engineering University of Nebraska Omaha NE 68182 United States Chinese Academy of Sciences Research Center on Data Mining and Knowledge Management Beijing 100039 China

ISBN: (纸本)9781604235531

Optimization-based algorithms, such as Multi-Criteria Linear programming (MCLP), have shown their effectiveness in classification. Nevertheless, due to the limitation of computation power and memory, it is difficult to apply MCLP, or similar optimization methods, to huge datasets. As the size of today's databases is continuously increasing, it is highly important that data mining algorithms are able to perform their functions regardless of dataset sizes. The objectives of this paper are: (1) to propose a new stratified random sampling and majority-vote ensemble approach, and (2) to compare this approach with the plain MCLP approach (which uses only part of the training set), and See5 (which is a decision-tree-based classification tool designed to analyze substantial datasets), on KDD99 and KDD2004 datasets. The results indicate that this new approach not only has the potential to handle arbitrary-size of datasets, but also outperforms the plain MCLP approach and achieves comparable classification accuracy to See5.

关键词： Classification (of information)

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Logsonomy - A Search Engine Folksonomy

Logsonomy - A Search Engine Folksonomy

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2nd International Conference on Weblogs and Social Media, ICWSM 2008

作者： Jäschke, Robert Krause, Beate Hotho, Andreas Stumme, Gerd Knowledge and Data Engineering Group University of Kassel Wilhelmshöher Allee 73 34121 Kassel Germany Research Center L3S Appelstr. 9a 30167 Hannover Germany

ISBN: (纸本)9781577353553

In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy ***. Copyright © 2008, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Search engines

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Subgroup discovery as a method for generating test ontologies

Subgroup discovery as a method for generating test ontologie...

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Learning, knowledge, Adaptation Workshops, LWA 2015: knowledge Discovery, data Mining and Machine Learning, KDML 2015, knowledge Management, FGWM 2015, Information Retrieval, IR 2015 and database Systems, FGDB 2015

作者： Knoell, Daniel Rieder, Constantin Atzmueller, Martin Scherer, Klaus Peter Karlsruhe Institute of Technology Eggenstein-LeopoldshafenD-76344 Germany University of Kassel Research Center for Information System Design Knowledge and Data Engineering Group Wilhelmshöher Allee 73 Kassel34121 Germany

For the validation of ontologies, test cases can be applied. In order to ease the acquisition of such test cases, automatic methods can be used. This paper introduces such an approach to unit testing of ontologies using subgroup discovery methods. The tests are given by small ontologies, which will be compared to an existing ontology. The generation of the test ontologies uses subgroup discovery methods to derive concepts and relations from real data which refers to the ontology. For each concept of the ontology, we apply subgroup discovery in order to identify subgroup patterns consisting of concepts that are (un-) correlated with the target concept. Then, subgroups with very high (or low) confidence will be selected for the testing ontology. We exemplify the approach using real-world data from the ophthalmology domain, and present first results and experiences of a case study demonstrating the test case generation process. Copyright © 2015 by the paper's authors.

关键词： Ontology

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Using Machine Learning with Pyspark for Solving a Big data Problem: Searching for Particles in High Energy Physics with Big Mass (HEPMASS) 7th

Using Machine Learning with Pyspark for Solving a Big Dat...

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7th International Conference on Smart City Applications, SCA 2022

作者： Azhari, Mourad Ahaji, Khalid Abarda, Abdallah Ettaki, Badia Zerouaoui, Jamal Center of Guidance and Planning Rabat Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and Knowledge Engineering School of Information Sciences Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9783031268519

The search for exotic particles is one of the most challenging topics for physicists. This work aims to resolve the Big data problem in the exotic particle area using the Apache Spark environment with the MLlib library. For this purpose, we analyze and discuss the performance of the following methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT). The results are very encouraging and prove that these proposed methods run efficiently with an Accuracy exceeding (83%). While the Gradient Boosted Tree (GBT) reaches a high AUC score 85%, better than Decision Tree (DT) and Random Forest. In this manuscript, we attempt to solve a big data problem using the "HEPMASS" dataset. Our data includes 10.5 million examples collected from the UCI website for the experimental phase. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Interactive Collaborative Exploration using Incomplete Contexts

arXiv

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arXiv 2019年

作者： Felde, Maximilian Stumme, Gerd Knowledge and Data Engineering Group Research Center for Information System Design University of Kassel Germany

A well-known knowledge acquisition method in the field of Formal Concept Analysis (FCA) is attribute exploration. It is used to reveal dependencies in a set of attributes with help of a domain expert. In most applications no single expert is capable (time- and knowledge-wise) of exploring the knowledge domain alone. However, there is up to now no theory that models the interaction of multiple experts for the task of attribute exploration with incomplete knowledge. To this end, we to develop a theoretical framework that allows multiple experts to explore domains together. We use a representation of incomplete knowledge as three-valued contexts. We then adapt the corresponding version of attribute exploration to fit the setting of multiple experts. We suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. In particular, we define an order that allows to compare the results of different exploration strategies on the same task with respect to their information completeness. Furthermore we discuss other ways of comparing collaboration strategies and suggest avenues for future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Formal concept analysis

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