標(biāo)題: Titlebook: Database and Expert Systems Applications; 29th International C Sven Hartmann,Hui Ma,Roland R. Wagner Conference proceedings 2018 Springer N [打印本頁(yè)] 作者: charter 時(shí)間: 2025-3-21 18:15
書(shū)目名稱(chēng)Database and Expert Systems Applications影響因子(影響力)
書(shū)目名稱(chēng)Database and Expert Systems Applications影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Database and Expert Systems Applications網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Database and Expert Systems Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Database and Expert Systems Applications被引頻次
書(shū)目名稱(chēng)Database and Expert Systems Applications被引頻次學(xué)科排名
書(shū)目名稱(chēng)Database and Expert Systems Applications年度引用
書(shū)目名稱(chēng)Database and Expert Systems Applications年度引用學(xué)科排名
書(shū)目名稱(chēng)Database and Expert Systems Applications讀者反饋
書(shū)目名稱(chēng)Database and Expert Systems Applications讀者反饋學(xué)科排名
作者: 精致 時(shí)間: 2025-3-21 23:50
Continuum Analysis of Biological Systemsterference variables into state space models. In addition, to realize the trend shift model efficiently, we propose an effective trend detection method, TP-TBSM (two-phase TBSM), by extending TBSM (trend-based segmentation method). The experimental results validate the proposed model and method.作者: Foreshadow 時(shí)間: 2025-3-22 01:04 作者: 預(yù)測(cè) 時(shí)間: 2025-3-22 07:46
https://doi.org/10.1007/978-3-540-74298-2ata centers..We implemented BuckTop and compared its performance for processing top-k queries over encrypted data with that of the popular threshold algorithm (TA) over original (plaintext) data. The results show the effectiveness of BuckTop for outsourcing sensitive data in the cloud and answering top-k queries.作者: Abutment 時(shí)間: 2025-3-22 10:24
ScaleSCAN: Scalable Density-Based Graph Clusterings as those of SCAN with much shorter computation time. Extensive experiments on both real-world and synthetic graphs demonstrate that the performance superiority of ScaleSCAN over the state-of-the-art methods.作者: 存在主義 時(shí)間: 2025-3-22 15:27
Answering Top-k Queries over Outsourced Sensitive Data in the Cloudata centers..We implemented BuckTop and compared its performance for processing top-k queries over encrypted data with that of the popular threshold algorithm (TA) over original (plaintext) data. The results show the effectiveness of BuckTop for outsourcing sensitive data in the cloud and answering top-k queries.作者: 存在主義 時(shí)間: 2025-3-22 20:28
Stochastic Modelling and Applied Probabilityee different sequence related approaches: process mining, dependency graph and sequential pattern mining. Then, we evaluate the impact of the recommender system. The result shows that all can improve the performance of students while the approach based on dependency graph contributes most.作者: 創(chuàng)新 時(shí)間: 2025-3-23 00:09 作者: GLEAN 時(shí)間: 2025-3-23 04:39
https://doi.org/10.1007/978-3-540-74298-2real-life setting. Our approach combines the trust, determined by the reputation of the provider, and the QoS. We present different algorithms for processing such selection queries and evaluate them through a set of experiments.作者: ETHER 時(shí)間: 2025-3-23 08:02 作者: 比賽用背帶 時(shí)間: 2025-3-23 09:49
BFASTDC: A Bitwise Algorithm for Mining Denial Constraintse dataset. This paper presents BFASTDC, a bitwise version of FASTDC that uses logical operations to form the auxiliary data structures from which DCs are mined. Our experimental study shows that BFASTDC can be more than one order of magnitude faster than FASTDC.作者: Adornment 時(shí)間: 2025-3-23 16:55 作者: 假設(shè) 時(shí)間: 2025-3-23 21:58
0302-9743 Expert Systems Applications, DEXA 2018, held in Regensburg, Germany, in September 2018..The 35 revised full papers presented together with 40 short papers were carefully reviewed and selected from 160 submissions. The papers of the first volume discuss a range of topics including: Big data analytics作者: 笨拙的我 時(shí)間: 2025-3-23 23:34 作者: Derogate 時(shí)間: 2025-3-24 03:21 作者: forbid 時(shí)間: 2025-3-24 08:54
BOUNCER: Privacy-Aware Query Processing over Federations of RDF Datasetsof the entities in an RDF dataset and their privacy regulations. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over RDF datasets that not only contain the relevant entities to answer a query, but that are also regulated by policies that 作者: accessory 時(shí)間: 2025-3-24 14:18 作者: cacophony 時(shí)間: 2025-3-24 16:12 作者: 客觀 時(shí)間: 2025-3-24 21:31
Efficient Aggregation Query Processing for Large-Scale Multidimensional Data by Combining RDB and KVdivided into several subsets called grids, and the aggregated values for each grid are precomputed. This technique improves query processing performance by reducing the amount of scanned data. We evaluated the efficiency of the proposed method by comparing its performance with current state-of-the-a作者: 苦笑 時(shí)間: 2025-3-25 00:19
Learning Interpretable Entity Representation in Linked Dataper proposes RWRDoc, an RWR (random walk with restart)-based representation learning method which learns representations of entities by weighted combinations of minimal representations of whole reachable entities w.r.t. RWR. Comprehensive experiments on diverse applications (such as ad-hoc entity se作者: 才能 時(shí)間: 2025-3-25 06:06
GARUM: A Semantic Similarity Measure Based on Machine Learning and Entity Characteristicsent domains, e.g., networks of proteins and media news. In the experimental study, GARUM exhibits higher correlation with gold standards than studied existing approaches. Thus, these results suggest that similarity measures should not consider . in isolation; contrary, combinations of these characte作者: SSRIS 時(shí)間: 2025-3-25 10:14
Knowledge Graphs for Semantically Integrating Cyber-Physical Systems . on a benchmark of AutomationML documents describing CPS components from various perspectives. Results suggest that . enables not only the semantic integration of the descriptions of CPS components, but also allows for preserving the individual characterization of these components.作者: SPASM 時(shí)間: 2025-3-25 14:37
Conference proceedings 2018ion; data warehouses and recommender systems; data streams; information networks and algorithms; database system architecture and performance; novel database solutions; graph querying and databases; learning; emerging applications;data mining; privacy; and text processing.作者: 猛擊 時(shí)間: 2025-3-25 18:27 作者: DOSE 時(shí)間: 2025-3-25 22:31
Asymptotic Normality and Exponential Boundsof the entities in an RDF dataset and their privacy regulations. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over RDF datasets that not only contain the relevant entities to answer a query, but that are also regulated by policies that 作者: faculty 時(shí)間: 2025-3-26 00:38
Asymptotic Normality and Exponential Boundsy. By using in-memory processing to handle the attribute selection procedure, we significantly reduce the processing time required. We evaluated the effectiveness of our proposed approach with an enriched dataset drawn from multiple real-world data sources, and augmented with synthetic values genera作者: 音樂(lè)會(huì) 時(shí)間: 2025-3-26 04:41 作者: 機(jī)制 時(shí)間: 2025-3-26 11:01
Continuum Analysis of Biological Systemsdivided into several subsets called grids, and the aggregated values for each grid are precomputed. This technique improves query processing performance by reducing the amount of scanned data. We evaluated the efficiency of the proposed method by comparing its performance with current state-of-the-a作者: 帽子 時(shí)間: 2025-3-26 12:48
Continuum Analysis of Biological Systemsper proposes RWRDoc, an RWR (random walk with restart)-based representation learning method which learns representations of entities by weighted combinations of minimal representations of whole reachable entities w.r.t. RWR. Comprehensive experiments on diverse applications (such as ad-hoc entity se作者: Feigned 時(shí)間: 2025-3-26 18:26 作者: dainty 時(shí)間: 2025-3-26 21:59 作者: 離開(kāi)可分裂 時(shí)間: 2025-3-27 04:44 作者: impale 時(shí)間: 2025-3-27 08:40 作者: palliate 時(shí)間: 2025-3-27 10:15
ScaleSCAN: Scalable Density-Based Graph Clusteringis one of the fundamental graph clustering algorithms that can find densely connected nodes as clusters. Although SCAN is used in many applications due to its effectiveness, it is computationally expensive to apply SCAN to large-scale graphs since SCAN needs to compute all nodes and edges. In this p作者: organism 時(shí)間: 2025-3-27 13:43 作者: mettlesome 時(shí)間: 2025-3-27 19:52
BFASTDC: A Bitwise Algorithm for Mining Denial Constraints vice versa. Denial constraints (DCs) are known to be a response to this expressiveness issue because they generalize important types of ICs, such as functional dependencies (FDs), conditional FDs, and check constraints. In this regard, automatic DC discovery is essential to avoid the expensive and 作者: leniency 時(shí)間: 2025-3-28 01:59 作者: 入伍儀式 時(shí)間: 2025-3-28 05:10 作者: 使迷惑 時(shí)間: 2025-3-28 08:13
A Diversification-Aware Itemset Placement Framework for Long-Term Sustainability of Retail Businesse, it becomes a necessity for retailers to place appropriate itemsets in a limited . number of premium slots in retail stores for achieving the goals of revenue maximization and itemset diversification. In this regard, research efforts are being made to extract itemsets with high utility for maximizi作者: antiandrogen 時(shí)間: 2025-3-28 12:40 作者: 不要不誠(chéng)實(shí) 時(shí)間: 2025-3-28 14:41
Efficient Aggregation Query Processing for Large-Scale Multidimensional Data by Combining RDB and KVologies have led to the generation of a large amount of multidimensional data, such as sensor data. Aggregation queries play an important role in analyzing such data. Although relational databases (RDBs) support efficient aggregation queries with indexes that enable faster query processing, increasi作者: 爵士樂(lè) 時(shí)間: 2025-3-28 22:38
Learning Interpretable Entity Representation in Linked Dataing representations of entities is required for various applications such as information retrieval and data mining. Entity representations can be roughly classified into two categories; (1) interpretable representations, and (2) latent representations. Interpretability of learned representations is 作者: Tdd526 時(shí)間: 2025-3-29 01:15
GARUM: A Semantic Similarity Measure Based on Machine Learning and Entity Characteristicss. Several . tasks, e.g., ranking, clustering, or link discovery, require for determining the relatedness between knowledge graph entities. However, state-of-the-art similarity measures may not consider all the characteristics of an entity to determine entity relatedness. We address the problem of s作者: 避開(kāi) 時(shí)間: 2025-3-29 06:29
Knowledge Graphs for Semantically Integrating Cyber-Physical Systemserent engineering perspectives (e.g., mechanical, electrical, and software). Standards related to Smart Manufacturing (e.g., AutomationML) are used to describe CPS components, as well as to facilitate their integration. Albeit expressive, smart manufacturing standards allow for the representation of作者: Glossy 時(shí)間: 2025-3-29 10:00 作者: hidebound 時(shí)間: 2025-3-29 15:26
Answering Top-k Queries over Outsourced Sensitive Data in the Cloud the outsourced data is not guaranteed by the cloud providers. One solution for protecting the user data is to encrypt it before sending to the cloud. Then, the main problem is to evaluate user queries over the encrypted data..In this paper, we consider the problem of answering top-k queries over en作者: Pander 時(shí)間: 2025-3-29 15:46 作者: 軟膏 時(shí)間: 2025-3-29 20:51
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/263465.jpg作者: Hypomania 時(shí)間: 2025-3-30 02:55
https://doi.org/10.1007/978-3-319-98809-2big data analytics; cloud data; clustering algorithms; computer networks; data mining; data privacy; datab作者: innovation 時(shí)間: 2025-3-30 07:36 作者: 解開(kāi) 時(shí)間: 2025-3-30 10:15 作者: medium 時(shí)間: 2025-3-30 16:15 作者: 呼吸 時(shí)間: 2025-3-30 20:01
