In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.

/pdf/models-and-issues-in-data-stream-systems-3aa6f5lsfv.pdf

Models and issues in data stream systems

Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.

Fake News Detection on Social Media: A Data Mining Perspective

Computational geometry

Increasingly pervasive networks are leading towards a world where data is constantly in motion. In such a world, conventional techniques for query processing, which were developed under the assumption of a far more static and predictable computational environment, will not be sufficient. Instead, query processors based on adaptive dataflow will be necessary. The Telegraph project has developed a suite of novel technologies for continuously adaptive query processing. The next generation Telegraph system, called TelegraphCQ, is focused on meeting the challenges that arise in handling large streams of continuous queries over high-volume, highly-variable data streams. In this paper, we describe the system architecture and its underlying technology, and report on our ongoing implementation effort, which leverages the PostgreSQL open source code base. We also discuss open issues and our research agenda.

http://db.csail.mit.edu/madden/html/TCQcidr03.pdf

TelegraphCQ: Continuous Dataflow Processing for an Uncertain World.

Distributed data processing is becoming a reality. Businesses want to do it for many reasons, and they often must do it in order to stay competitive. While much of the infrastructure for distributed data processing is already there (e.g., modern network technology), a number of issues make distributed data processing still a complex undertaking: (1) distributed systems can become very large, involving thousands of heterogeneous sites including PCs and mainframe server machines; (2) the state of a distributed system changes rapidly because the load of sites varies over time and new sites are added to the system; (3) legacy systems need to be integrated—such legacy systems usually have not been designed for distributed data processing and now need to interact with other (modern) systems in a distributed environment. This paper presents the state of the art of query processing for distributed database and information systems. The paper presents the “textbook” architecture for distributed query processing and a series of techniques that are particularly useful for distributed database systems. These techniques include special join techniques, techniques to exploit intraquery paralleli sm, techniques to reduce communication costs, and techniques to exploit caching and replication of data. Furthermore, the paper discusses different kinds of distributed systems such as client-server, middleware (multitier), and heterogeneous database systems, and shows how query processing works in these systems.

/pdf/the-state-of-the-art-in-distributed-query-processing-2b23xet25f.pdf

The state of the art in distributed query processing

An audio-based system may perform noise and echo suppression by initially processing an audio signal that is subject to acoustic echo or echo resulting from other system characteristics. The audio signal is processed in the time domain using an adaptive echo-cancellation filter. The audio is then further processed in the frequency domain to simultaneously reduce background noise and residual echo.

Cross-domain processing for noise and echo suppression

We propose an effective adaptive null-forming scheme for two nearby microphones in endfire orientation that are used in digital hearing aids and in many other hearing devices. This adaptive null-forming scheme is mainly based on an adaptive combination of two fixed polar patterns that act to make the null of the combined polar pattern of the system output always be toward the direction of the noise. The adaptive combination of these two fixed polar patterns is accomplished by simply updating an adaptive gain following the output of the first polar pattern unit. The value of this gain is updated by minimizing the power of the system output, and related adaptive algorithms to update this gain are also given in this paper. We have implemented this proposed system on the basis of a programmable DSP chip and performed various tests. Theoretical analyses and testing results demonstrated the effectiveness of the proposed system and the accuracy of its implementation.

Adaptive Null-Forming Scheme in

We are delighted to present this special issue of the Journal of Data and Information Quality (ACM JDIQ) on Combating Digital Misinformation and Disinformation. This issue presents an overview of innovative research primarily at the intersection of information credibility, machine learning, and data science, from theory to practice, with a focus on combating misinformation and disinformation. Spread of misinformation and disinformation is one of the most serious challenges facing the news industry, and a threat to democracy worldwide. The problem has reached an unprecedented level via social media, where contents can be created and disseminated to a large audience with little to zero cost and revenues are driven by clicks. Researchers from multiple disciplines have proposed various strategies, built automated and semiautomated systems [1, 3], and recommended policy changes across the media ecosystem [2, 4]. Recently, researchers also explored how artificial intelligence techniques, particularly machine learning and natural language processing, can be leveraged to combat falsehoods online. In this special issue of JDIQ, we provide a representative collection of insightful articles at the intersection of data quality and credibility, from theory to practice, with a focus on improvements in veracity and value. The articles went through a rigorous procedure of review involving at least three expert reviewers for each article. After two rounds of review, we selected five articles that made contributions to both research and practice. Zannettou et al., in “The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans,” provide a typology of the false information content on the Web and surveys the latest research directions. It identifies several lines of works in the false information ecosystem. In particular, it surveys the research works from false information propagation, perception, and identification perspectives. Then, the authors specifically attend the false information spread in the political domain and investigate the velocity and consequence of the spread in communities. Finally, the authors delineate several future research directions that can help understand and mitigate this misinformation problem.

https://dl.acm.org/doi/pdf/10.1145/3321484

Introduction to the Special Issue on Combating Digital Misinformation and Disinformation

An audio-based system may perform echo cancellation by decomposing an input audio signal and a corresponding reference signal into sub-signals corresponding to different frequency bands and implementing adaptive filtering independently for each frequency band. Computational resources may be allocated differently for each of the frequency bands depending on observed conditions. Orders of adaptive FIR filters may be varied during operation. Double talk detection and echo path change detection may be implemented for some frequency bands and not others. Filter adaption rates may also be assigned independently to each of the FIR filters, and may be changed during operation.

Non-uniform adaptive echo cancellation

XML plays an important role in delivering data over the Internet, and the need to store and manipulate XML in its native format has become increasingly relevant. This growing need necessitates work on developing native XML operators, especially for one as fundamental as sort. We present NEXSORT, an algorithm that leverages the hierarchical nature of XML to efficiently sort an XML document in external memory. In a fully sorted XML document, children of every nonleaf element are ordered according to a given sorting criterion. Among NEXSORT's uses is in combination with structural merge as the XML version of sort-merge join, which allows us to merge large XML documents using only a single pass once they are sorted. The hierarchical structure of an XML document limits the number of possible legal orderings among its elements, which means that sorting XML is fundamentally "easier" than sorting a flat file. We prove that the I/O lower bound for sorting XML in external memory is /spl Theta/(max{n,nlog/sub m/(k/B)}), where n is the number of blocks in the input XML document, m is the number of main memory blocks available for sorting, B is the number of elements that can fit in one block, and k is the maximum fan-out of the input document tree. We show that NEXSORT performs within a constant factor of this theoretical lower bound. In practice we demonstrate, even with a naive implementation, NEXSORT significantly outperforms a regular external merge sort of all elements by their key paths, unless the XML document is nearly flat, in which case NEXSORT degenerates essentially to external merge sort.

Jun Yang

Papers

Cross-domain processing for noise and echo suppression

Adaptive Null-Forming Scheme in

Introduction to the Special Issue on Combating Digital Misinformation and Disinformation

Non-uniform adaptive echo cancellation

NEXSORT: sorting XML in external memory