Web information system (WIS) is frequently-used and indispensable in daily social life. WIS provides information services in many scenarios, such as electronic commerce, communities, and edutainment. Data cleaning plays an essential role in various WIS scenarios to improve the quality of data service. In this paper, we present a review of the state-of-the-art methods for data cleaning in WIS. According to the characteristics of data cleaning, we extract the critical elements of WIS, such as interactive objects, application scenarios, and core technology, to classify the existing works. Then, after elaborating and analyzing each category, we summarize the descriptions and challenges of data cleaning methods with sub-elements such as data & user interaction, data quality rule, model, crowdsourcing, and privacy preservation. Finally, we analyze various types of problems and provide suggestions for future research on data cleaning in WIS from the technology and interactive perspective.

https://www.techscience.com/cmc/v62n3/38341/pdf

A Review of Data Cleaning Methods for Web Information System

Aiming at the research of resource allocation and interference management in multi-layer wireless networks in heterogeneous cognitive networks, this paper mainly discusses the resource allocation and interference management in heterogeneous cognitive networks and proposes our proposed cluster-based cooperative interference management scheme. This scheme minimizes the cross-layer interference of multiple base station cells to primary users and avoids the same-layer interference between base station cells through joint channel allocation and power allocation. Through cognitive perception or joint scheduling, the available channel can be obtained by the home base station cell, and then the channel, power and other resources can be allocated according to the related algorithms proposed by us, so as to ensure the harmonious coexistence of the home base station cell and the authorization system such as macrocell. Finally, we validate the proposed algorithm. The simulation results show that the algorithms are effective in reducing interference, but the degree is different.

/pdf/resource-allocation-and-interference-management-for-multi-264ksaavio.pdf

Resource allocation and interference management for multi-layer wireless networks in heterogeneous cognitive networks

With the real-time recording of the health care system, real-time monitoring of user trajectory data can effectively protect the health and health of the body. In order to accurately establish the spatial position trajectory data prediction model, a space trajectory data prediction model based on variable space chaotic particle swarm optimization (CPSO)-optimized least squares support vector machine (LSSVM) is proposed. Firstly, aiming at the problem that particle swarm optimization (PSO) is easy to fall into local optimum, the global optimization performance of PSO algorithm is improved by using variable space chaos search strategy and particle out-of-bounds mirror processing strategy. Numerical simulation experiments verify the superiority of improved particle swarm optimization algorithm. Based on the CPSO algorithm, the hyper parameters of the CPSO-optimized least squares support vector machine (CPSO-LSSVM) are proposed to improve the prediction accuracy of the model. Finally, using GeoLife and T-Drive dataset as the research object, the CPSO-LSSVM spatial position prediction model is established by using the trajectory data of the dataset. The simulation experiment verifies that the CPSO-LSSVM positional space prediction model has higher prediction accuracy and more strong generalization ability to accurately and effectively predict spatial location.

Improved particle swarm optimization LSSVM spatial location trajectory data prediction model in health care monitoring system

Entity disambiguation involves mapping mentions in texts to the corresponding entities in a given knowledge base. Most previous approaches were based on handcrafted features and failed to capture s...

Collective Entity Disambiguation Based on Hierarchical Semantic Similarity

Increased data generation has led to an increase in the availability of rich information online. However, complications occur in the form of heterogeneity in the data storage. In order to have complete information, all the data sources must be utilized. Hence a data integration mechanism is required. However, integrating heterogeneous data leads to conflicting data in the system. This paper presents a fast and efficient mechanism to identify and resolve conflicts on huge streaming data using Spark. A wrapper based query formulation module constructs queries depending on the underlying data sources. The retrieved data is converted to a structured format and similarity between the data is identified, followed by distributed conflict identification and resolution. Experiments were conducted on streaming data. Effective conflict detections and a speed up from ~589 seconds to 10 seconds was achieved.

/pdf/fast-and-efficient-conflict-identification-and-resolution-in-8b3k6u5yk7.pdf

Fast and Efficient Conflict Identification and Resolution in Huge Streaming Data

With the continuous development of information technology, various multi-media data are constantly emerging and presents the characteristics of autonomous and heterogeneous, how to integrate and analysis data more correctly and efficiently has become a challenging problem. Firstly, in order to improve the quality of the integrated data, two real-time threads combined with data adapter are used to monitor and refresh necessary updates from heterogeneous data efficiently. Once the original data has been updated, the real-time data will be loaded into the data center soon. Secondly, a data reverse cleaning method is proposed to improve the data quality. It uses the data source tree that built in the data integration process to find the location of the original data quickly after reverse cleaning. finally, a data accuracy assessment algorithm is designed for data quality assessment, which is based on Bayesian network and the path condition algorithm. Experimental results show that the quality of the integrated data significantly higher than the quality of the original data.

An optimized data integration model based on reverse cleaning for heterogeneous multi-media data

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Hao Chen

Author Tools

Chat about Author

Papers

An optimized data integration model based on reverse cleaning for heterogeneous multi-media data