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.

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

The state of the art in distributed query processing

The amount of information available to large-scale enterprises is growing rapidly. New information is being generated continuously by operational sources such as order processing, inventory control, and customer service systems. To support efficient analysis and mining of such diverse, distributed information, a data warehousing system collects data from multiple sources and stores the integrated information in a central repository. The data warehouse needs to be updated continuously to reflect source data updates. 
We address the problem of supporting temporal information efficiently in data warehousing systems. Users of a data warehouse often are interested not only in monitoring the current information, but also in analyzing the history in order to predict future trends. We present a temporal data warehousing framework in which we can create and incrementally update temporal views over the history of the source data, even when sources do not support temporal operations. Keeping temporal data warehouses up-to-date is complex, because temporal views may need to be updated not only when source data is updated but also as time advances, and these two dimensions of change interact in subtle ways. We present efficient techniques for maintaining temporal data warehouses without disturbing source operations. A related challenge is supporting large-scale temporal aggregation operations in data warehouses. We introduce new data structures that facilitate incremental computation and update of various types of temporal aggregates. 
We also describe the implementation of TIP (Temporal Information Processor), an extension within a commercial database system that supports a full range of temporal query capabilities. Applications of TIP include a powerful graphical browser for temporal data, and a temporal data warehousing prototype supporting incremental maintenance based on the algorithms we developed.

Temporal data warehousing

A universal reconfigurable system and method are provided for reducing nonlinear echo and residual echo, and for echo leakage prevention in various time-varying and complex environments is proposed in this invention. According to one embodiment, an echo cancellation system includes (1) an adaptive linear filter implemented in either time-domain or frequency-domain; (2) a nonlinear echo suppression; (3) echo leakage prevention; (4) direct current (DC), low frequency residual echo and noise reduction; (5) time-domain nonlinear processor (NLP) to reduce the residual echo; and (6) frequency-domain NLP to further reduce the residual echo. The echo cancellation system is universally applicable to acoustic echo cancellation (AEC) and/or electrical echo cancellation applications. In terms of AEC, this invention is reconfigurable for one or more microphones and/or one or more reference channels. The numbers of microphones and reference channels are user configurable.

A universal reconfigurable echo cancellation system

This paper considers the problem of scalably processing a large number of continuous queries. We propose a flexible framework with novel data structures and algorithms for group-processing and indexing continuous queries by exploiting potential overlaps in query predicates. Our approach partitions the collection of continuous queries into groups based on the clustering patterns of the query ranges, and then applies specialized processing strategies to those heavily-clustered groups (or hotspots). To maintain the partition dynamically, we present efficient algorithms that maintain a nearly optimal partition in nearly amortized logarithmic time. We show how to use the hotspots to scalably process large numbers of continuous select-join and band-join queries, which are much more challenging than simple range selection queries. Experiments demonstrate that this approach can improve the processing throughput by orders of magnitude. As another application of hotspots, we show how to use them to build a high-quality histogram for intervals in linear time.

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Scalable continuous query processing by tracking hotspots

A system and method are disclosed for effective and simple stereo widening over arbitrarily-configured speakers and its real-time implementation. According to one embodiment, the system includes five processing units: (1) elevation processing unit; (2) side signal or difference signal processing unit; (3) center signal processing unit; (4) binaural signal processing unit; and (5) stereo limiter (to prevent the clipping) unit. Any of the five processing units may be included or omitted depending on the application. In general, embodiments of the present invention provide effective and simple stereo widening schemes with good audio quality results, but without having substantial requirements on the speakers' positional configuration. For example, the proposed schemes can apply to both symmetric and non-symmetric stereo loudspeakers.

Stereo widening over arbitrarily-configured loudspeakers

Techniques for suppressing large echo due to nonlinearity, instable adaptive filter, and so on. An echo cancellation system includes an echo canceller unit, a nonlinear echo detection unit, and a nonlinear echo suppression unit. The echo canceller unit (e.g., an adaptive filter) receives a reference signal and a near-end signal, derives an echo estimate signal based on the reference and near-end signals, cancels a portion of echo in the near-end signal based on the echo estimate signal, and provides an intermediate signal having residual echo. The nonlinear echo detection unit detects for large nonlinear echo in the echo estimate signal. The nonlinear echo suppression unit suppresses large nonlinear echo in the first intermediate signal based on a variable gain, which may be adjusted lower if large nonlinear echo is detected and higher otherwise.

Jun Yang

Papers

Temporal data warehousing

A universal reconfigurable echo cancellation system

Scalable continuous query processing by tracking hotspots

Stereo widening over arbitrarily-configured loudspeakers

Method and system for nonlinear echo suppression