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Divide and conquer algorithm is a common strategy applied in big data. Model averaging has the natural divide-and-conquer feature, but its theory has not been developed in big data scenarios. The goal of this paper is to fill this gap. We propose two ...

Nonparametric regression imputation is commonly used in missing data analysis. However, it su_ers from the "curse of dimension". The problem can be alleviated by the explosive sample size in the era of big data, while the large-scale data size presents ...

Vertical Federated Learning (FL) handles decentralized and partitioned vertically data about common entities. While most existing privacy-preserving federated learning algorithms require a third party (TP) as an intermediary data accessor to coordinate ...

We address the privacy-preserving data-sharing problem in a distributed multiparty setting. In this setting, each data site owns a distinct part of a dataset and the aim is to estimate the parameters of a statistical model conditioned on the complete ...

Many recommender systems use association rules mining, a technique that captures relations between user interests and recommends new probable ones accordingly. Applying association rule mining causes privacy concerns as user interests may contain ...

In this article, we focus on the problem of learning a Bayesian network over distributed data stored in a commodity cluster. Specifically, we address the challenge of computing the scoring function over distributed data in an efficient and scalable ...

The paper describes an approach to parallelization of Normal Bayes classifier training algorithm for distributed data. In the process of distributed data analysis and the algorithm performance, the results fail to join properly. Due to this, the ...

A sketch or synopsis of a large dataset captures vital properties of the original data while typically occupying much less space. In this paper, we consider the problem of computing a sketch of a massive data matrix A ∈ℜ^nxd, which is distributed across ...

In the current era of big data, high volumes of a wide variety of valuable data can be easily collected and generated from a broad range of data sources of different veracities at a high velocity. Due to the well-known 5V's of these big data, many ...

This paper gives a first attempt to answer the following general question: Given a set of machines connected by a point-to-point communication network, each having a {\em noisy} dataset, how can we perform communication-efficient statistical estimations ...

A MapReduce scheduling algorithm plays a critical role in managing large clusters of hardware nodes and meeting multiple quality requirements by controlling the order and distribution of users, jobs, and tasks execution. A comprehensive and structured ...

Data collected for recommendation purposes might be distributed among various e-commerce sites, which can collaboratively provide more accurate predictions. However, because of privacy concerns, they might not want to work together. If privacy measures ...

Data summarization is an effective approach to dealing with the "big data" problem. While data summarization problems traditionally have been studied is the streaming model, the focus is starting to shift to distributed models, as distributed/parallel ...

Kriging is one of the most preferred geostatistical methods in many engineering fields. Basically, it creates a model using statistical properties of all measured points in the region, where a prediction value is sought. The accuracy of the kriging ...

The Webdam ERC grant is a five-year project that started in December 2008. The goal is to develop a formal model for Web data management that would open new horizons for the development of the Web in a well-principled way, enhancing its functionality, ...

DBSCAN is a well-known density-based clustering algorithm which offers advantages for finding clusters of arbitrary shapes compared to partitioning and hierarchical clustering methods. However, there are few papers studying the DBSCAN algorithm under ...

In this paper, we present an approach to access the distributed data for interoperability in the distributed environments based on the multi-agent system that is designed on the proposed structure of multi-agent by FIPA(IEEE Foundation for Intelligent ...

With the proliferation of organizations that independently collect various types of data, with the growing awareness of corporations and public to keep their sensitive data private, and with the ever-increasing need of government and corporate policy ...

Providing recommendations on trusts between entities is receiving increasing attention lately. Customers may prefer different online vendors for shopping. Thus, their preferences about various products might be distributed among multiple parties. To ...

Contingency tables are widely used in many fields to analyze the relationship or infer the association between two or more variables. Indeed, due to their simplicity and ease, they are one of the first methods used to analyze gathered data. Typically, ...