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.

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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.

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The state of the art in distributed query processing

Methods and devices for reducing intermodulation distortion are described herein. In response to receiving an audio input signal, N filtered audio signals may be generated by a filter bank, corresponding to N different frequency bands. The N filtered audio signals may be delayed by a delay time D, and a determination of whether an audio frame of the filtered audio signals includes an audio event or a non-audio event. A signal level estimation may then be determined, the signal level estimation indicating an expander, a compressor, or no compression effects being present. An amount of gain is determined and applied to the delayed audio signals, which are summed across the N frequency bands to generate a full-band audio signal. In some embodiments, the full-band audio signal may be applied to a limiter to reduce any audio clipping, and a final audio signal may be generated.

Systems and methods for reducing intermodulation distortion

We study the problem of assigning subscribers to brokers in a wide-area content-based publish/subscribe system. A good assignment should consider both subscriber interests in the event space and subscriber locations in the network space, and balance multiple performance criteria including bandwidth, delay, and load balance. The resulting optimization problem is NP-complete, so systems have turned to heuristics and/or simpler algorithms that ignore some performance criteria. Evaluating these approaches has been challenging because optimal solutions remain elusive for realistic problem sizes. To enable proper evaluation, we develop a Monte Carlo approximation algorithm with good theoretical properties and robustness to workload variations. To make it computationally feasible, we combine the ideas of linear programming, randomized rounding, coreset, and iterative reweighted sampling. We demonstrate how to use this algorithm as a yardstick to evaluate other algorithms, and why it is better than other choices of yardsticks. With its help, we show that a simple greedy algorithm works well for a number of workloads, including one generated from publicly available statistics on Google Groups. We hope that our algorithms are not only useful in their own right, but our principled approach toward evaluation will also be useful in future evaluation of solutions to similar problems in content-based publish/subscribe.

Subscriber Assignment for Wide-Area Content-Based Publish/Subscribe

An audio-based system may perform automatic noise reduction to enhance speech intelligibility in an audio signal. Described techniques include initially analyzing audio frames in the time domain to identify frames having relatively low power levels. Those frames are then further analyzed in the frequency domain to estimate noise. For example, the initially identified frames may be analyzed at each of multiple frequencies to detect the lowest exhibited power at each of those frequencies. The lowest power values are used as an estimation of noise across the frequency spectrum, and as the basis for calculating a spectral gain for filtering the audio signal in the frequency domain.

Cross-domain filtering for audio noise reduction

We study the optimization problem of selecting numerical quantities to clean in order to fact-check claims based on such data. Oftentimes, such claims are technically correct, but they can still mislead for two reasons. First, data may contain uncertainty and errors. Second, data can be "fished" to advance particular positions. In practice, fact-checkers cannot afford to clean all data and must choose to clean what "matters the most" to checking a claim. We explore alternative definitions of what "matters the most": one is to ascertain claim qualities (by minimizing uncertainty in these measures), while an alternative is just to counter the claim (by maximizing the probability of finding a counterargument). We show whether the two objectives align with each other, with important implications on when fact-checkers should exercise care in selective data cleaning, to avoid potential bias introduced by their desire to counter claims. We develop efficient algorithms for solving the various variants of the optimization problem, showing significant improvements over naive solutions. The problem is particularly challenging because the objectives in the fact-checking context are complex, non-linear functions over data. We obtain results that generalize to a large class of functions, with potential applications beyond fact-checking.

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Selecting data to clean for fact checking: minimizing uncertainty vs. maximizing surprise

Objective Methylation of the promoters of SHOX2 and RASSF1A are potentially informative biomarkers for the diagnosis of early lung adenocarcinoma (LUAD). Abnormal methylation of SHOX2 and RASSF1A promoters may promote the occurrence and facilitate the progression of LUAD. Materials and Methods We selected 54 patients with early LUAD and 31 patients with benign lung nodules as a NJDT cohort and evaluated their DNA methylation and mRNA sequencing levels. The DNA methylation sequencing, mRNA sequencing, and clinical data for patients with LUAD were obtained from The Cancer Genome Atlas, and served as a TCGA cohort. We evaluated the diagnostic potential of a SHOX2 and RASSF1A combined promoter methylation assay for detection of early LUAD in the NJDT cohort. Then we explored the promoter methylation levels of SHOX2 and RASSF1A and their gene expression between normal and tumor samples at different stages in both cohorts. Pathways enriched between tumor and normal samples of methylation-positive patients in the NJDT cohort were analyzed. Results In the NJDT cohort, the sensitivity of the combined promoter methylation assay on tumor samples was 74.07%, the sensitivity on paired tumor and paracancerous samples was 77.78%, and the specificities in both contexts were 100%. The combined promoter methylation-positive patients had clinicopathologic features including older age, larger tumors, deeper invasion, and higher Ki-67 expression. In both cohorts, SHOX2 expression increased and RASSF1A expression decreased in tumor samples. The promoter methylation level of SHOX2 and RASSF1A was significantly higher in tumor samples at stage I-II than that in normal samples. The promoter methylation levels of these two genes were both negative associated with their expression in early tumor samples. In the NJDT cohort, methylation-positive patients of both individual SHOX2 and RASSF1A assays exhibited upregulation of folate acid metabolism and nucleotide metabolism in tumor samples. The SHOX2 methylation-positive and RASSF1A methylation-positive patients showed the downregulation of pathways related to cell proliferation and apoptosis and pathways involved in DNA repair, cell growth and cell adhesion, respectively. Conclusion The combined promoter methylation assay for SHOX2 and RASSF1A can be used for screening and diagnosis of early LUAD, with good sensitivity and specificity. The promoter methylation levels of SHOX2 and RASSF1A were associated with their abnormal mRNA expression, and affected DNA instability, cell proliferation, apoptosis and tumor microenvironment in patients with LUAD.

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Jun Yang

Papers

Systems and methods for reducing intermodulation distortion

Subscriber Assignment for Wide-Area Content-Based Publish/Subscribe

Cross-domain filtering for audio noise reduction

Selecting data to clean for fact checking: minimizing uncertainty vs. maximizing surprise

The Diagnostic Potential of SHOX2 and RASSF1A DNA Methylation in Early Lung Adenocarcinoma