当前期刊: Knowledge and Information Systems Go to current issue    加入关注   
显示样式:        排序: IF: - GO 导出
我的关注
我的收藏
您暂时未登录!
登录
  • EAFIM: efficient apriori-based frequent itemset mining algorithm on Spark for big transactional data
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-04-07
    Shashi Raj, Dharavath Ramesh, M. Sreenu, Krishan Kumar Sethi

    Abstract Frequent itemset mining is considered a popular tool to discover knowledge from transactional datasets. It also serves as the basis for association rule mining. Several algorithms have been proposed to find frequent patterns in which the apriori algorithm is considered as the earliest proposed. Apriori has two significant bottlenecks associated with it: first, repeated scanning of input dataset

    更新日期:2020-04-08
  • A novel possibilistic artificial immune-based classifier for course learning outcome enhancement
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-04-04
    Ilyes Jenhani, Ammar Elhassan, Ghassen Ben Brahim

    Abstract In this paper, we propose PAIRS3: a possibilistic classification approach based on artificial immune recognition system (AIRS) and the possibility theory. PAIRS3 is applied to address shortcomings in student attainment rates of course learning outcomes by predicting effective remedial actions through learning from assessment rubrics instances. For most of assessment rubric instances, it is

    更新日期:2020-04-06
  • Bayesian network classifiers using ensembles and smoothing
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-30
    He Zhang, François Petitjean, Wray Buntine

    Abstract Bayesian network classifiers are, functionally, an interesting class of models, because they can be learnt out-of-core, i.e. without needing to hold the whole training data in main memory. The selective K-dependence Bayesian network classifier (SKDB) is state of the art in this class of models and has shown to rival random forest (RF) on problems with categorical data. In this paper, we introduce

    更新日期:2020-03-31
  • A survey on influence maximization in a social network
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-29
    Suman Banerjee, Mamata Jenamani, Dilip Kumar Pratihar

    Abstract Given a social network with diffusion probabilities as edge weights and a positive integer k, which k nodes should be chosen for initial injection of information to maximize the influence in the network? This problem is popularly known as the Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain, since one and

    更新日期:2020-03-31
  • Recommender systems with selfish users
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-28
    Maria Halkidi, Iordanis Koutsopoulos

    Abstract Recommender systems are a fundamental component of contemporary social-media platforms and require feedback submitted from users in order to fulfill their goal. On the other hand, the raise of advocacy about user-controlled data repositories supports the selective submission of data by user through intelligent software agents residing at the user end. These agents are endowed with the task

    更新日期:2020-03-28
  • Measuring time-sensitive user influence in Twitter
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-28
    Behzad Rezaie, Morteza Zahedi, Hoda Mashayekhi

    Abstract Identification of the influential users is one of the most practical analyses in social networks. The importance of this analysis stems from the fact that such users can affect their followers “/friends” viewpoints. This study aims at introducing two new indices to identify the most influential users in the Twitter social network. Four sets of features extracted from user activities, user

    更新日期:2020-03-28
  • Feature extraction from null and non-null spaces of kernel local discriminant embedding
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-27
    A. Bosaghzadeh, F. Dornaika

    Abstract Extracting discriminative features and reducing the dimensionality of data are two main objectives of manifold learning. Among different techniques, nonlinear manifold learning methods have been proposed in order to extract features from data which are not linearly distributed. Kernel trick is one of the famous nonlinear techniques which helps to project the data without an explicit mapping

    更新日期:2020-03-27
  • Cyber security incidents analysis and classification in a case study of Korean enterprises
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-27
    Alaa Mohasseb, Benjamin Aziz, Jeyong Jung, Julak Lee

    Abstract The increasing amount and complexity of Cyber security attacks in recent years have made text analysis and data mining techniques an important factor in discovering features of such attacks and detecting future security threats. In this paper, we report on the results of a recent case study that involved the analysis of a community data set collected from five small and medium companies in

    更新日期:2020-03-27
  • Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-26
    Jonathan R. Wells, Sunil Aryal, Kai Ming Ting

    Abstract Existing distance metric learning methods require optimisation to learn a feature space to transform data—this makes them computationally expensive in large datasets. In classification tasks, they make use of class information to learn an appropriate feature space. In this paper, we present a simple supervised dissimilarity measure which does not require learning or optimisation. It uses class

    更新日期:2020-03-27
  • Lexifield: a system for the automatic building of lexicons by semantic expansion of short word lists
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-20
    Suzanne Mpouli, Michel Beigbeder, Christine Largeron

    Abstract We present Lexifield, a fully automatic language-independent system for building domain-specific lexicons from a short list of terms defining the domain. Lexifield relies on a pre-trained word embedding model, a definition dictionary and a dictionary of synonyms. To evaluate this system, four lexicons have been generated: one lexicon in French for the topic “son” (“sound”) and three lexicons

    更新日期:2020-03-20
  • A survey of state-of-the-art approaches for emotion recognition in text
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-18
    Nourah Alswaidan, Mohamed El Bachir Menai

    Abstract Emotion recognition in text is an important natural language processing (NLP) task whose solution can benefit several applications in different fields, including data mining, e-learning, information filtering systems, human–computer interaction, and psychology. Explicit emotion recognition in text is the most addressed problem in the literature. The solution to this problem is mainly based

    更新日期:2020-03-20
  • CAMAR: a broad learning based context-aware recommender for mobile applications
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-14
    Tingting Liang, Lifang He, Chun-Ta Lu, Liang Chen, Haochao Ying, Philip S. Yu, Jian Wu

    Abstract The emergence of a large number of mobile apps brings challenges to locate appropriate apps for users, which makes mobile app recommendation an imperative task. In this paper, we first conduct detailed data analysis to show the characteristics of mobile apps which are different with conventional items (e.g., movies, books). Considering the specific property of mobile apps, we propose a broad

    更新日期:2020-03-20
  • Improved covering-based collaborative filtering for new users’ personalized recommendations
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-13
    Zhipeng Zhang, Yasuo Kudo, Tetsuya Murai, Yonggong Ren

    Abstract User-based collaborative filtering (UBCF) is widely used in recommender systems (RSs) as one of the most successful approaches, but traditional UBCF cannot provide recommendations with satisfactory accuracy and diversity simultaneously. Covering-based collaborative filtering (CBCF) is a useful approach that we have proposed in our previous work, which greatly improves the traditional UBCF

    更新日期:2020-03-20
  • Online anomaly search in time series: significant online discords
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-09
    Paolo Avogadro, Luca Palonca, Matteo Alessandro Dominoni

    Abstract The aim of this work is to obtain a useful anomaly definition for online analysis of time series. The idea is to develop an anomaly concept which is sustainable for long-lived and frequent streamings. As a solution, we provide an adaptation of the discord concept, which has been successfully used for anomaly detection on time series. An online approach implies the frequent processing of a

    更新日期:2020-03-20
  • Identifying at-risk students based on the phased prediction model
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-06-24
    Yan Chen, Qinghua Zheng, Shuguang Ji, Feng Tian, Haiping Zhu, Min Liu

    Abstract Identifying at-risk students is one of the most important issues in online education. During different stages of a semester, students display various online learning behaviors. Therefore, we propose a phased prediction model to predict at-risk students at different stages of a semester. We analyze students’ individual characteristics and online learning behaviors, extract features that are

    更新日期:2020-03-20
  • Local low-rank Hawkes processes for modeling temporal user–item interactions
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-07-09
    Jin Shang, Mingxuan Sun

    Abstract Hawkes processes have become very popular in modeling multiple recurrent user–item interaction events that exhibit mutual-excitation properties in various domains. Generally, modeling the interaction sequence of each user–item pair as an independent Hawkes process is ineffective since the prediction accuracy of future event occurrences for users and items with few observed interactions is

    更新日期:2020-03-20
  • Kernel conditional clustering and kernel conditional semi-supervised learning
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-06-06
    Xiao He, Thomas Gumbsch, Damian Roqueiro, Karsten Borgwardt

    Abstract The results of clustering are often affected by covariates that are independent of the clusters one would like to discover. Traditionally, alternative clustering algorithms can be used to solve such clustering problems. However, these suffer from at least one of the following problems: (1) Continuous covariates or nonlinearly separable clusters cannot be handled; (2) assumptions are made about

    更新日期:2020-03-20
  • Nearest base-neighbor search on spatial datasets
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-04-10
    Hong-Jun Jang, Kyeong-Seok Hyun, Jaehwa Chung, Soon-Young Jung

    Abstract This paper presents a nearest base-neighbor (NBN) search that can be applied to a clustered nearest neighbor problem on spatial datasets with static properties. Given two sets of data points R and S, a query point q, distance threshold δ and cardinality threshold k, the NBN query retrieves a nearest point r (called the base-point) in R where more than k points in S are located within the distance

    更新日期:2020-03-20
  • Dynamically updating approximations based on multi-threshold tolerance relation in incomplete interval-valued decision information systems
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-07-03
    Bingyan Lin, Xiaoyan Zhang, Weihua Xu, Yanxue Wu

    Abstract With the development of society, data noise and other factors will cause the incompleteness of information systems. Objects may increase or decrease over time in information systems. The classical information system can be extended to the incomplete interval-valued decision information system (IIDIS) that is the researching object of this paper. Incremental learning technique is a significant

    更新日期:2020-03-20
  • Two approaches for clustering algorithms with relational-based data
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-07-23
    João C. Xavier-Junior, Anne M. P. Canuto, Luiz M. G. Gonçalves

    Abstract It is well known that relational databases still play an important role for many companies around the world. For this reason, the use of data mining methods to discover knowledge in large relational databases has become an interesting research issue. In the context of unsupervised data mining, for instance, the conventional clustering algorithms cannot handle the particularities of the relational

    更新日期:2020-03-20
  • Evidential positive opinion influence measures for viral marketing
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-07-02
    Siwar Jendoubi, Arnaud Martin

    Abstract The viral marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large cascade of propagation and adoptions. In this paper, we will introduce an evidential opinion-based influence maximization model for viral marketing. Besides, our approach tackles

    更新日期:2020-03-20
  • Exploiting patterns to explain individual predictions
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-06-08
    Yunzhe Jia, James Bailey, Kotagiri Ramamohanarao, Christopher Leckie, Xingjun Ma

    Abstract Users need to understand the predictions of a classifier, especially when decisions based on the predictions can have severe consequences. The explanation of a prediction reveals the reason why a classifier makes a certain prediction, and it helps users to accept or reject the prediction with greater confidence. This paper proposes an explanation method called Pattern Aided Local Explanation

    更新日期:2020-03-20
  • A systematic framework of predicting customer revisit with in-store sensors
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-06-29
    Sundong Kim, Jae-Gil Lee

    Abstract Recently, there is a growing number of off-line stores that are willing to conduct customer behavior analysis. In particular, predicting revisit intention is of prime importance, because converting first-time visitors to loyal customers is very profitable. Thanks to noninvasive monitoring, shopping behaviors and revisit statistics become available from a large proportion of customers who turn

    更新日期:2020-03-20
  • Framework for extreme imbalance classification: SWIM—sampling with the majority class
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-07-17
    Colin Bellinger, Shiven Sharma, Nathalie Japkowicz, Osmar R. Zaïane

    Abstract The class imbalance problem is a pervasive issue in many real-world domains. Oversampling methods that inflate the rare class by generating synthetic data are amongst the most popular techniques for resolving class imbalance. However, they concentrate on the characteristics of the minority class and use them to guide the oversampling process. By completely overlooking the majority class, they

    更新日期:2020-03-20
  • High average-utility sequential pattern mining based on uncertain databases
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-07-22
    Jerry Chun-Wei Lin, Ting Li, Matin Pirouz, Ji Zhang, Philippe Fournier-Viger

    Abstract The emergence and proliferation of the internet of things (IoT) devices have resulted in the generation of big and uncertain data due to the varied accuracy and decay of sensors and their different sensitivity ranges. Since data uncertainty plays an important role in IoT data, mining the useful information from uncertain dataset has become an important issue in recent decades. Past works focus

    更新日期:2020-03-20
  • Accelerating pattern-based time series classification: a linear time and space string mining approach
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-07-12
    Atif Raza, Stefan Kramer

    Abstract Subsequences-based time series classification algorithms provide interpretable and generally more accurate classification models compared to the nearest neighbor approach, albeit at a considerably higher computational cost. A number of discretized time series-based algorithms have been proposed to reduce the computational complexity of these algorithms; however, the asymptotic time complexity

    更新日期:2020-03-20
  • Integrating learned and explicit document features for reputation monitoring in social media
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-07-19
    Fernando Giner, Enrique Amigó, Felisa Verdejo

    Abstract Currently, monitoring reputation in social media is probably one of the most lucrative applications of information retrieval methods. However, this task poses new challenges due to the dynamicity of contents and the need for early detection of topics that affect the reputations of companies. Addressing this problem with learning mechanisms that are based on training data sets is challenging

    更新日期:2020-03-20
  • Information-preserving abstractions of event data in process mining
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-07-20
    Sander J. J. Leemans, Dirk Fahland

    Abstract Process mining aims at obtaining information about processes by analysing their past executions in event logs, event streams, or databases. Discovering a process model from a finite amount of event data thereby has to correctly infer infinitely many unseen behaviours. Thereby, many process discovery techniques leverage abstractions on the finite event data to infer and preserve behavioural

    更新日期:2020-03-20
  • SUM-optimal histograms for approximate query processing
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-03-06
    Meifan Zhang, Hongzhi Wang, Jianzhong Li, Hong Gao

    Abstract In this paper, we study the problem of the SUM query approximation with histograms. We define a new kind of histogram called the SUM-optimal histogram which can provide better estimation result for the SUM queries than the traditional equi-depth and V-optimal histograms. We propose three methods for the histogram construction. The first one is a dynamic programming method, and the other two

    更新日期:2020-03-07
  • L-BiX: incremental sliding-window aggregation over data streams using linear bidirectional aggregating indexes
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-21
    Savong Bou, Hiroyuki Kitagawa, Toshiyuki Amagasa

    Abstract The number of real-time information sources, or so-called streams, has rapidly increased, leading to a greater demand for complex analyses over streams. Although many stream analysis methods exist, aggregation is fundamental to ascertain higher levels of knowledge from raw data. In particular, sliding-window aggregation, where aggregations over sliding windows are repeatedly computed, is useful

    更新日期:2020-03-07
  • Decision support for personalized hospital choice using the DEX hierarchical model with SMAA
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-20
    Yi Chen, Shuai Ding, Handong Zheng, Yanchun Zhang, Shanlin Yang

    Abstract Despite an ever-growing personalized demand for patients’ hospital choice, little systematic work has examined the decision process that considers the diversity of medical service demand. In this paper, we develop an intelligence decision framework to explore multi-source uncertain information in hospital choice. The framework employs a novel SMAA-DEX method to generate a ranking list of hospital

    更新日期:2020-03-07
  • Supervised learning as an inverse problem based on non-smooth loss function
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-20
    Soufiane Lyaqini, Mohamed Quafafou, Mourad Nachaoui, Abdelkrim Chakib

    Abstract This paper is concerned by solving supervised machine learning problem as an inverse problem. Recently, many works have focused on defining a relationship between supervised learning and the well-known inverse problems. However, this connection between the learning problem and the inverse one has been done in the particular case where the inverse problem is reformulated as a minimization problem

    更新日期:2020-03-07
  • Exploiting review embedding and user attention for item recommendation
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-19
    Yatong Sun, Guibing Guo, Xu Chen, Penghai Zhang, Xingwei Wang

    Abstract As a valuable source of user preferences and item properties, reviews have been widely leveraged in many approaches to enhance the performance of recommender systems. Although encouraging success has been obtained, there are two more weaknesses need to be addressed. (1) Most approaches represent users or items merely based on the modeling of review texts, but ignore the potential and latent

    更新日期:2020-03-07
  • Random walk-based entity representation learning and re-ranking for entity search
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-18
    Takahiro Komamizu

    Abstract Linked Data (LD) has become a valuable source of factual records, and entity search is a fundamental task in LD. The task is, given a query consisting of a set of keywords, to retrieve a set of relevant entities in LD. The state-of-the-art approaches for entity search are based on information retrieval techniques. This paper first examines these approaches with a traditional evaluation metric

    更新日期:2020-03-07
  • On spatial keyword covering
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-18
    Dong-Wan Choi, Jian Pei, Xuemin Lin

    Abstract This article introduces and solves a spatial keyword cover problem (SK-Cover for short), which aims to identify the group of spatio-textual objects covering all the keywords in a query and minimizing a distance cost function that leads to fewer objects in the answer set. In a broad sense, SK-Cover has been actively studied in the literature of spatial keyword search, such as the m-closest

    更新日期:2020-03-07
  • Enhancing supervised bug localization with metadata and stack-trace
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-12
    Yaojing Wang, Yuan Yao, Hanghang Tong, Xuan Huo, Ming Li, Feng Xu, Jian Lu

    Abstract Locating relevant source files for a given bug report is an important task in software development and maintenance. To make the locating process easier, information retrieval methods have been widely used to compute the content similarities between bug reports and source files. In addition to content similarities, various other sources of information such as the metadata and the stack-trace

    更新日期:2020-03-07
  • PragmaticOIE: a pragmatic open information extraction for Portuguese language
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-10
    Cleiton Fernando Lima Sena, Daniela Barreiro Claro

    Abstract Information extraction (IE) involves the extraction of useful facts from texts. IE approaches have been categorized into two types: Traditional IE and Open IE. Traditional IE recognizes a predefined set of relationships between the arguments, and it has typically been applied to specific domains. Open IE extracts relationship descriptors expressing any semantic relationship between a pair

    更新日期:2020-03-07
  • A compact firefly algorithm for matching biomedical ontologies
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-08
    Xingsi Xue

    Abstract Biomedical ontologies have gained particular relevance in the life science domain due to its prominent role in representing knowledge in this domain. However, the existing biomedical ontologies could define the same biomedical concept in different ways, which yields the biomedical ontology heterogeneous problem. To implement the inter-operability among the biomedical ontologies, it is critical

    更新日期:2020-03-07
  • PCA-based drift and shift quantification framework for multidimensional data
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-06
    Igor Goldenberg, Geoffrey I. Webb

    Abstract Concept drift is a serious problem confronting machine learning systems in a dynamic and ever-changing world. In order to manage concept drift it may be useful to first quantify it by measuring the distance between distributions that generate data before and after a drift. There is a paucity of methods to do so in the case of multidimensional numeric data. This paper provides an in-depth analysis

    更新日期:2020-03-07
  • The examination of the effect of the criterion for neural network’s learning on the effectiveness of the qualitative analysis of multidimensional data
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2020-02-03
    Dariusz Jamróz

    Abstract A variety of multidimensional visualization methods are applied for the qualitative analysis of multidimensional data. One of the multidimensional data visualization methods is a method using autoassociative neural networks. In order to perform visualizations of n-dimensional data, such a network has n inputs, n outputs and one of the interlayers consisting of two outputs whose values represent

    更新日期:2020-03-07
  • Dynamic clustering of interval data based on hybrid $$L_q$$Lq distance
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-05-17
    Leandro Carlos de Souza, Renata Maria Cardoso Rodrigues de Souza, Getúlio José Amorim do Amaral

    Abstract Dynamic clustering defines partitions within data and prototypes to each partition. Distance metrics are responsible for checking the closeness between instances and prototypes. Considering the literature about interval data, distances depend on interval bounds and the information inside the intervals is ignored. This paper proposes new distances, which explore the information inside of intervals

    更新日期:2020-03-07
  • Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-04-02
    Ibrahim Aljarah, Majdi Mafarja, Ali Asghar Heidari, Hossam Faris, Seyedali Mirjalili

    Abstract Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms inspired from the social collaboration and team hunting activities of grey wolves in nature. This algorithm benefits from stochastic operators, but it is still prone to stagnation in local optima and premature convergence when solving problems with a large number of variables (e.g., clustering problems)

    更新日期:2020-03-07
  • Relevant feature selection and ensemble classifier design using bi-objective genetic algorithm
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-03-05
    Asit Kumar Das, Soumen Kumar Pati, Arka Ghosh

    Abstract In the era of digital boom, single classifier cannot perform well in various datasets. Ensemble classifier aims to bridge this performance gap by combining multiple classifiers of diverse characteristics to get better generalization. But classifier selection highly depends on the dataset, and its efficiency degrades tremendously due to the presence of irrelevant features. Feature selection

    更新日期:2020-03-07
  • Anomaly detection of event sequences using multiple temporal resolutions and Markov chains
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-05-15
    Martin Boldt, Anton Borg, Selim Ickin, Jörgen Gustafsson

    Abstract Streaming data services, such as video-on-demand, are getting increasingly more popular, and they are expected to account for more than 80% of all Internet traffic in 2020. In this context, it is important for streaming service providers to detect deviations in service requests due to issues or changing end-user behaviors in order to ensure that end-users experience high quality in the provided

    更新日期:2020-03-07
  • Generating synthetic positive and negative business process traces through abduction
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-06-20
    Daniela Loreti, Federico Chesani, Anna Ciampolini, Paola Mello

    Abstract As recent years have seen the rise of a new discipline commonly addressed as process mining, focused on the management of business processes, two tasks have gained increasing attention in research: process discovery and compliance monitoring. In both these fields, the demand for event log benchmarks with predefined characteristics has determined the design of various methodologies and tools

    更新日期:2020-03-07
  • Measures of uncertainty for knowledge bases
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-05-07
    Zhaowen Li, Gangqiang Zhang, Wei-Zhi Wu, Ningxin Xie

    Abstract This paper investigates measures of uncertainty for knowledge bases by using their knowledge structures. Knowledge structures of knowledge bases are first introduced. Then, dependence and independence between knowledge structures of knowledge bases are proposed, which are characterized by inclusion degree. Next, measures of uncertainty for a given knowledge base are studied, and it is proved

    更新日期:2020-03-07
  • HEEL: exploratory entity linking for heterogeneous information networks
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-04-01
    Chengyu Wang, Xiaofeng He, Aoying Zhou

    Abstract A heterogeneous information network (HIN) is a ubiquitous data model, consisting of multiple types of entities and relations. Names of entities in HINs are inherently ambiguous, making it difficult to fully disambiguate a HIN. In this paper, we introduce the task of exploratory entity linking for HINs. Given a partially disambiguated HIN, we aim at linking ambiguous names to disambiguated

    更新日期:2020-03-07
  • Automatic attribute construction for basketball modelling
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2019-04-13
    Petar Vračar, Erik Štrumbelj, Igor Kononenko

    Abstract We address the problem of automatic extraction of patterns in the sequence of events in basketball games and construction of statistical models for generating a plausible simulation of a match between two distinct teams. We present a method for automatic construction of an attribute space which requires very little expert knowledge. The attributes are defined as the ratio between the number

    更新日期:2020-03-07
  • Crowd labeling latent Dirichlet allocation.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2018-11-13
    Luca Pion-Tonachini,Scott Makeig,Ken Kreutz-Delgado

    Large, unlabeled datasets are abundant nowadays, but getting labels for those datasets can be expensive and time-consuming. Crowd labeling is a crowdsourcing approach for gathering such labels from workers whose suggestions are not always accurate. While a variety of algorithms exist for this purpose, we present crowd labeling latent Dirichlet allocation (CL-LDA), a generalization of latent Dirichlet

    更新日期:2019-11-01
  • Binary Classifier Calibration Using an Ensemble of Piecewise Linear Regression Models.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2018-04-03
    Mahdi Pakdaman Naeini,Gregory F Cooper

    In this paper we present a new non-parametric calibration method called ensemble of near isotonic regression (ENIR). The method can be considered as an extension of BBQ (Pakdaman Naeini, Cooper and Hauskrecht, 2015b), a recently proposed calibration method, as well as the commonly used calibration method based on isotonic regression (IsoRegC) (Zadrozny and Elkan, 2002). ENIR is designed to address

    更新日期:2019-11-01
  • Markov Logic Networks for Adverse Drug Event Extraction from Text.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2017-11-11
    Sriraam Natarajan,Vishal Bangera,Tushar Khot,Jose Picado,Anurag Wazalwar,Vitor Santos Costa,David Page,Michael Caldwell

    Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies

    更新日期:2019-11-01
  • Collegial Activity Learning between Heterogeneous Sensors.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2017-10-11
    Kyle D Feuz,Diane J Cook

    Activity recognition algorithms have matured and become more ubiquitous in recent years. However, these algorithms are typically customized for a particular sensor platform. In this paper we introduce PECO, a Personalized activity ECOsystem, that transfers learned activity information seamlessly between sensor platforms in real time so that any available sensor can continue to track activities without

    更新日期:2019-11-01
  • A Survey of Methods for Time Series Change Point Detection.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2017-06-13
    Samaneh Aminikhanghahi,Diane J Cook

    Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes

    更新日期:2019-11-01
  • Diffusion archeology for diffusion progression history reconstruction.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2016-11-09
    Emre Sefer,Carl Kingsford

    Diffusion through graphs can be used to model many real-world processes, such as the spread of diseases, social network memes, computer viruses, or water contaminants. Often, a real-world diffusion cannot be directly observed while it is occurring - perhaps it is not noticed until some time has passed, continuous monitoring is too costly, or privacy concerns limit data access. This leads to the need

    更新日期:2019-11-01
  • Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2016-10-18
    Yelong Shen,NhatHai Phan,Xiao Xiao,Ruoming Jin,Junfeng Sun,Brigitte Piniewski,David Kil,Dejing Dou

    Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals'

    更新日期:2019-11-01
  • Transfer Learning for Class Imbalance Problems with Inadequate Data.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2016-07-06
    Samir Al-Stouhi,Chandan K Reddy

    A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data is not readily available

    更新日期:2019-11-01
  • An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2016-01-12
    Iyad Batal,Gregory Cooper,Dmitriy Fradkin,James Harrison,Fabian Moerchen,Milos Hauskrecht

    This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs

    更新日期:2019-11-01
  • Indexing Volumetric Shapes with Matching and Packing.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2015-06-19
    David Ryan Koes,Carlos J Camacho

    We describe a novel algorithm for bulk-loading an index with high-dimensional data and apply it to the problem of volumetric shape matching. Our matching and packing algorithm is a general approach for packing data according to a similarity metric. First an approximate k-nearest neighbor graph is constructed using vantage-point initialization, an improvement to previous work that decreases construction

    更新日期:2019-11-01
  • Efficient Mining of Discriminative Co-clusters from Gene Expression Data.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2015-02-03
    Omar Odibat,Chandan K Reddy

    Discriminative models are used to analyze the differences between two classes and to identify class-specific patterns. Most of the existing discriminative models depend on using the entire feature space to compute the discriminative patterns for each class. Co-clustering has been proposed to capture the patterns that are correlated in a subset of features, but it cannot handle discriminative patterns

    更新日期:2019-11-01
  • Hyper-structure mining of frequent patterns in uncertain data streams.
    Knowl. Inf. Syst. (IF 2.397) Pub Date : 2014-04-15
    Chandima Hewanadungodage,Yuni Xia,Jaehwan John Lee,Yi-Cheng Tu

    Data uncertainty is inherent in many real-world applications such as sensor monitoring systems, location-based services, and medical diagnostic systems. Moreover, many real-world applications are now capable of producing continuous, unbounded data streams. During the recent years, new methods have been developed to find frequent patterns in uncertain databases; nevertheless, very limited work has been

    更新日期:2019-11-01
Contents have been reproduced by permission of the publishers.
导出
全部期刊列表>>
聚焦肿瘤,探索癌症
欢迎探索2019年最具下载量的材料科学论文
论文语言润色服务
宅家赢大奖
如何将化学应用到可持续发展目标中
向世界展示您的会议墙报和演示文稿
全球疫情及响应:BMC Medicine专题征稿
新版X-MOL期刊搜索和高级搜索功能介绍
化学材料学全球高引用
ACS材料视界
x-mol收录
自然科研论文编辑服务
南方科技大学
南方科技大学
舒伟
中国科学院长春应化所于聪-4-8
复旦大学
课题组网站
X-MOL
香港大学化学系刘俊治
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug