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Malevolent Activity Detection with Hypergraph-Based Models

IEEE TRANSACTIONS ON knowledge AND data engineering 2017年第5期29卷 1115-1128页

作者： Guzzo, Antonella Pugliese, Andrea Sacca, Domenico Piccolo, Antonio Univ Calabria DIMES Dept I-87036 Arcavacata Di Rende Italy

We propose a hypergraph-based framework for modeling and detecting malevolent activities. The proposed model supports the specification of order-independent sets of action symbols along with temporal and cardinality constraints on the execution of actions. We study and characterize the problems of consistency checking, equivalence, and minimality of hypergraph-based models. In addition, we define and characterize the general activity detection problem, that amounts to finding all subsequences that represent a malevolent activity in a sequence of logged actions. Since the problem is intractable, we also develop an index data structure that allows the security expert to efficiently extract occurrences of activities of interest.

关键词： Graphs and networks knowledge and data engineering tools and techniques

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knowledge Guided Attention and Graph Convolutional Networks for Chemical-Disease Relation Extraction

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第1期20卷 489-499页

作者： Sun, Yi Wang, Jian Lin, Hongfei Zhang, Yijia Yang, Zhihao Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

The automatic extraction of the chemical-disease relation (CDR) from the text becomes critical because it takes a lot of time and effort to extract valuable CDR manually. Studies have shown that prior knowledge from the biomedical knowledge base is important for relation extraction. The method of combining deep learning models with prior knowledge is worthy of our study. In this paper, we propose a new model called knowledge Guided Attention and Graph Convolutional Networks (KGAGN) for CDR extraction. First, to make full advantage of domain knowledge, we train entity embedding as a feature representation of input sequence, and relation embedding to capture weighted contextual information further through the attention mechanism. Then, to make full advantage of syntactic dependency information in cross-sentence CDR extraction, we construct document-level syntactic dependency graphs and encode them using a graph convolution network (GCN). Finally, the chemical-induced disease (CID) relation is extracted by using weighted context features and long-range dependency features both of which contain additional knowledge informationWe evaluated our model on the CDR dataset published by the BioCreative-V community and achieves an F1-score of 73.3%, surpassing other state-of-the-art methods. the code implemented by PyTorch 1.7.0 deep learning library can be downloaded from Github: https://***/sunyi123/cdr.

关键词： Text analysis Connectionism and neural nets knowledge and data engineering tools and techniques Attention

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A method for estimating the precision of placename matching

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IEEE TRANSACTIONS ON knowledge AND data engineering 2007年第8期19卷 1089-1101页

作者： Doerr, Martin Papagelis, Manos Fdn Res & Technol Hellas Inst Comp Sci GR-71110 Iraklion Crete Greece Univ Toronto Dept Comp Sci Toronto ON M5S 3G4 Canada

Information in digital libraries and information systems frequently refers to locations or objects in geographic space. Digital gazetteers are commonly employed to match the referred placenames with actual locations in information integration and data cleaning procedures. This process may fail due to missing information in the gazetteer, multiple matches, or false positive matches. We have analyzed the cases of success and reasons for failure of the mapping process to a gazetteer. Based on these, we present a statistical model that permits estimating 1) the completeness of a gazetteer with respect to the specific target area and application, 2) the expected precision and recall of one-to-one mappings of source placenames to the gazetteer, 3) the semantic inconsistency that remains in one-to-one mappings, and 4) the degree to which the precision and recall are improved under knowledge of the identity of higher levels in a hierarchy of places. The presented model is based on statistical analysis of the mapping process of a large set of placenames itself and does not require any other background data. The statistical model assumes that a gazetteer is populated by a stochastic process. The paper discusses how future work could take deviations from this assumption into account. The method has been applied to a real case.

关键词： data mapping knowledge and data engineering tools and techniques database integration data translation

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VisExPreS: A Visual Interactive Toolkit for User-Driven Evaluations of Embeddings

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022年第7期28卷 2791-2807页

作者： Ghosh, Aindrila Nashaat, Mona Miller, James Quader, Shaikh Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada IBM Toronto Software Lab Toronto ON L6G 1C7 Canada

Although popularly used in big-data analytics, dimensionality reduction is a complex, black-box technique whose outcome is difficult to interpret and evaluate. In recent years, a number of quantitative and visual methods have been proposed for analyzing low-dimensional embeddings. On the one hand, quantitative methods associate numeric identifiers to qualitative characteristics of these embeddings;and, on the other hand, visual techniques allow users to interactively explore these embeddings and make decisions. However, in the former case, users do not have control over the analysis, while in the latter case. assessment decisions are entirely dependent on the user's perception and expertise. In order to bridge the gap between the two, in this article, we present VisExPreS, a visual interactive toolkit that enables a user-driven assessment of low-dimensional embeddings. VisExPreS is based on three novel techniques namely PG-LAPS, PG-GAPS, and RepSubset, that generate interpretable explanations of the preserved local and global structures in embeddings. In the first two techniques, the VisExPreS system proactively guides users during every step of the analysis. We demonstrate the utility of VisExPreS in interpreting, analyzing, and evaluating embeddings from different dimensionality reduction algorithms using multiple case studies and an extensive user study.

关键词： Measurement Visualization Quality assessment data visualization Stress Distortion Linear programming data and knowledge visualization interactive data exploration and discovery knowledge and data engineering tools and techniques

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Local Anomaly Detection for Multivariate Time Series by Temporal Dependency Based on Poisson Model

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第11期33卷 6701-6711页

作者： Benkabou, Seif-Eddine Benabdeslem, Khalid Kraus, Vivien Bourhis, Kilian Canitia, Bruno Univ Poitiers LIAS ISAE ENSMA F-86960 Poitiers France Univ Lyon 1 LIRIS F-69622 Villeurbanne France Lizeo IT F-69002 Lyon France

Multivariate time series data are invasive in different domains, ranging from data center supervision and e-commerce data to financial transactions. This kind of data presents an important challenge for anomaly detection due to the temporal dependency aspect of its observations. In this article, we investigate the problem of unsupervised local anomaly detection in multivariate time series data from temporal modeling and residual analysis perspectives. The residual analysis has been shown to be effective in classical anomaly detection problems. However, it is a nontrivial task in multivariate time series as the temporal dependency between the time series observations complicates the residual modeling process. Methodologically, we propose a unified learning framework to characterize the residuals and their coherence with the temporal aspect of the whole multivariate time series. Experiments on real-world datasets are provided showing the effectiveness of the proposed algorithm.

关键词： Time series analysis Anomaly detection Transmission line matrix methods Task analysis Sparse matrices Minimization Probabilistic logic knowledge and data engineering tools and techniques machine learning optimization time series analysis

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A new complicated-knowledge representation approach based on knowledge meshes

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IEEE TRANSACTIONS ON knowledge AND data engineering 2006年第1期18卷 47-62页

作者： Yan, HS Southeast Univ Res Inst Automat Nanjing 210096 Jiangsu Peoples R China

This paper presents a new complicated-knowledge representation method for the self-reconfiguration of complex systems such as complex software systems, complex manufacturing systems, and knowledgeable manufacturing systems. Herein, new concepts of a knowledge mesh (KM) and an agent mesh (AM) are proposed along with a new KM-based approach to complicated-knowledge representation. KM is the representation of such complicated macroknowledge as an advanced manufacturing mode, focusing on knowledge about the structure, functions, and information flows of an advanced manufacturing system. The multiple set, KM, and the mapping relationships between both, are then formally defined. The union, intersection, and minus operations on the multiple sets are proposed, and their properties proved. Then, the perfectness of a KM, the redundancy set between the two KMs, and the multiple redundancy set on the redundancy set are defined. Three examples are provided to illustrate the concepts-of the KM, multiple set, multiple redundancy set, and logical operations. On the basis of the above, the KM-based inference engine is presented. In logical operations on KMs, each KM is taken as an operand. A new KM obtained by operations on KM multiple sets can be mapped into an AM for automatic reconfiguration of complex software systems. Finally, the combination of two real management modes is exemplified for the effective application of the new KM-based method to the self-reconfiguration of complex systems. It is worth mentioning that KM multiple sets can also be taken as a new formal representation of software systems if their corresponding AMs are the real software systems.

关键词： knowledge and data engineering tools and techniques complicated knowledge representation knowledge mesh agent mesh formal representation of software systems

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Determining Attributes to Maximize Visibility of Objects

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IEEE TRANSACTIONS ON knowledge AND data engineering 2009年第7期21卷 959-973页

作者： Miah, Muhammed Das, Gautam Hristidis, Vagelis Mannila, Heikki Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA Florida Int Univ Sch Comp & Informat Sci Miami FL 33199 USA Aalto Univ HIIT FI-00014 Helsinki Finland Univ Helsinki FI-00014 Helsinki Finland

In recent years, there has been significant interest in the development of ranking functions and efficient top-k retrieval algorithms to help users in ad hoc search and retrieval in databases (e.g., buyers searching for products in a catalog). We introduce a complementary problem: How to guide a seller in selecting the best attributes of a new tuple (e.g., a new product) to highlight so that it stands out in the crowd of existing competitive products and is widely visible to the pool of potential buyers. We develop several formulations of this problem. Although the problems are NP-complete, we give several exact and approximation algorithms that work well in practice. One type of exact algorithms is based on Integer Programming (IP) formulations of the problems. Another class of exact methods is based on maximal frequent item set mining algorithms. The approximation algorithms are based on greedy heuristics. A detailed performance study illustrates the benefits of our methods on real and synthetic data.

关键词： data mining knowledge and data engineering tools and techniques marketing mining methods and algorithms retrieval models

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Application of Dempster-Schafer Method in Family-Based Association Studies

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013年第4期10卷 1071-1075页

作者： Rajabli, Farid Goktas, Unal Inan, Gul Turgut Ozal Univ Dept Elect & Elect Engn TR-06010 Ankara Turkey Turgut Ozal Univ Dept Comp Engn TR-06010 Ankara Turkey Middle E Tech Univ Dept Stat TR-06800 Ankara Turkey

In experiments designed for family-based association studies, methods such as transmission disequilibrium test require large number of trios to identify single-nucleotide polymorphisms associated with the disease. However, unavailability of a large number of trios is the Achilles' heel of many complex diseases, especially for late-onset diseases. In this paper, we propose a novel approach to this problem by means of the Dempster-Shafer method. The simulation studies show that the Dempster-Shafer method has a promising overall performance, in identifying single-nucleotide polymorphisms in the correct association class, as it has 90 percent accuracy even with 60 trios.

关键词： Algorithm design and analysis biology and genetics knowledge and data engineering tools and techniques probabilistic algorithms

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Extracting relevant attribute values for improved search

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IEEE INTERNET COMPUTING 2007年第5期11卷 26-35页

作者： Bergamaschi, Sonia Guerra, Francesco Orsini, Mirko Sartori, Claudio Univ Modena Informat & Commun Technol Doctorate Sch I-41100 Modena Italy Univ Bologna Rimini Branch I-40126 Bologna Italy

A new kind of metadata offers a synthesized view of an attribute's values for a user to exploit when creating or refining a search query in data-integration systems. The extraction technique that obtains these values is automatic and independent of an attribute domain but parameterized with various metrics for similarity measures. The authors describe a fully implemented prototype and some experimental results to show the effectiveness of "relevant values" when searching a knowledge base.

关键词： knowledge and data engineering tools and techniques Clustering Classification Association Rules Metadata Semantic knowledge Management

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Estimating micro-populations through social media analytics

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SOCIAL NETWORK ANALYSIS AND MINING 2017年第1期7卷 1-19页

作者： Sinnott, Richard O. Wang, Wei Univ Melbourne Dept Comp & Informat Syst Melbourne Vic 3010 Australia

Estimation of crowd sizes or the occupancy of buildings and skyscrapers can often be essential. However, traditional ways of estimation through manual counting, image processing or in the case of skyscrapers, through total water usage are awkward, inefficient and often inaccurate. Social media has developed rapidly in the last decade. In this work, we provide novel solutions to estimate the population of suburbs and skyscrapers-so-called micro-populations, through the use of social media. We develop a big data solution leveraging large-scale harvesting and analysis of Twitter data. By harvesting realtime tweets and clustering tweets within suburbs and skyscrapers, we show how micro-populations can be calculated. To validate this, we construct linear and spatial models for the suburbs in four cities of Australia using census data and geospatial data models (shapefiles). Our prediction of micro-population shows that Twitter can indeed be used for population prediction with a high degree of accuracy.

关键词： knowledge and data engineering tools and techniques Super (very large) computers Correlation and regression analysis

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