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

In many applications, the system needs to selectively present a small subset of answers to users. The set of all possible answers can be seen as an elevation surface over a domain, where the elevation measures the quality of each answer, and the dimensions of the domain correspond to attributes of the answers with which similarity between answers can be measured. This paper considers the problem of finding a diverse set of k high-quality representatives for such a surface. We show that existing methods for diversified top-k and weighted clustering problems are inadequate for this problem. We propose k-DHR as a better formulation for the problem. We show that k-DHR has a submodular and monotone objective function, and we develop efficient algorithms for solving k-DHR with provable guarantees. We conduct extensive experiments to demonstrate the usefulness of the results produced by k-DHR for applications in computational lead-finding and fact-checking, as well as the efficiency and effectiveness of our algorithms.

/pdf/finding-diverse-high-value-representatives-on-a-surface-of-542keqisc4.pdf

Finding diverse, high-value representatives on a surface of answers

Wireless sensor networks are widely used to continuously collect data from the environment. Because of energy constraints on battery-powered nodes, it is critical to minimize communication. Suppression has been proposed as a way to reduce communication by using predictive models to suppress reporting of predictable data. However, in the presence of communication failures, missing data are difficult to interpret because these could have been either suppressed or lost in transmission. There is no existing solution for handling failures for general, spatiotemporal suppression that uses cascading. While cascading further reduces communication, it makes failure handling difficult, because nodes can act on incomplete or incorrect information and in turn affect other nodes. We propose a cascaded suppression framework that exploits both temporal and spatial data correlation to reduce communication, and applies coding theory and Bayesian inference to recover missing data resulted from suppression and communication failures. Experiment results show that cascaded suppression significantly reduces communication cost and improves missing data recovery compared to existing approaches.

/pdf/failure-aware-cascaded-suppression-in-wireless-sensor-5ek47lk2c1.pdf

Failure-Aware Cascaded Suppression in Wireless Sensor Networks

We demonstrate I-Rex1, a system designed to help users understand SQL query evaluation and debug SQL queries. I-Rex lets users interactively "trace" the evaluation of complex SQL queries, including those with correlated subqueries. I-Rex also explains why a query returns an incorrect answer with respect to a reference query over a test database instance---a common use case in education and software regression testing. To avoid the cognitive overload caused by debugging over a large database instance, I-Rex lets users focus on smaller instances contained in the large one (which we call "counterexamples") that still distinguish the two queries. Supporting these features for SQL queries poses two key challenges. First, unlike debugging for procedural languages, it is not clear how to trace a declarative SQL query, because its execution plan often differs from how it was originally written. I-Rex offers a novel interface for tracing SQL query evaluation in a way faithful to how queries are written syntactically, even for complex queries involving multiple levels of nesting and correlation. Second, we need a method for finding small counterexamples that handles the complexity of practical SQL. I-Rex extends provenance support for SQL in non-trivial ways to work with various query constructs. This demonstration walks through use cases in which I-Rex helps users understand and debug SQL queries.

/pdf/i-rex-an-interactive-relational-query-explainer-for-sql-31bp0wf0gi.pdf

I-Rex: an interactive relational query explainer for SQL

A continuous query is a standing query over a dynamic data set whose query result needs to be constantly updated as new data arrive. We consider the problem of constructing a data structure on a set of continuous band-join queries over two data sets R and S, where each band-join query asks for reporting the set { (r,s)∈ R× S | a≤ r–s≤ b} for some parameters a and b, so that given a data update in R or S, one can quickly identify the subset of continuous queries whose results are affected by the update, and compute changes to these results.

We present the first nontrivial data structure for this problem that simultaneously achieves subquadratic space and sublinear query time. This is achieved by first decomposing the original problem into two independent subproblems, and then carefully designing data structures suitable for each case, by exploiting the particular structure in each subproblem.

A key step in the above construction is a data structure whose performance increases with the degree of clusteredness of the band-joins being indexed. We believe that this structure is of independent interest and should have broad impact in practice. We present the details in [1].

Monitoring continuous band-join queries over dynamic data

A microphone system using left and right microphones produces good binaural cues as well as noise reduction by using two adaptive filters (56, 58). A noise enhanced reference signal (46) is sent to two different adaptive filters (56, 58). The output of each adaptive filter is combined with the left or right microphone signal, respectively, to produce left and right output signals. These left and right output signals are used as the error signals to modify the coefficients of the adaptive filters (56, 58). By using two adaptive filters, each of the adaptive filters is able to independently operate helping to preserve the binaural cues.

Jun Yang

Papers

Finding diverse, high-value representatives on a surface of answers

Failure-Aware Cascaded Suppression in Wireless Sensor Networks

I-Rex: an interactive relational query explainer for SQL

Monitoring continuous band-join queries over dynamic data

Adaptive microphone array system with preserving binaural cues