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

Wireless sensor networks can be viewed as the integration of three subsystems: a low-impact in situdata acquisition and collection system, a system for inference of process models from observed data and a prioriinformation, and a system that controls the observation and collection. Each of these systems is connected by feedforward and feedback signals from the others; moreover, each subsystem is formed from behavioral components that are distributed among the sensors and out-of-network computational resources. Crucially, the overall performance of the system is constrained by the costs of energy, time, and computational complexity. We are addressing these design issues in the context of monitoring forest environments with the objective of inferring ecosystem process models. We describe here our framework of treating data and models jointly, and its application to soil moisture processes.

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From Data Reverence to Data Relevance: Model-Mediated Wireless Sensing of the Physical Environment

We present a system called RATest, designed to help debug relational queries against reference queries and test database instances. In many applications, e.g., classroom learning and regression testing, we test the correctness of a user query Q by evaluating it over a test database instance D and comparing its result with that of evaluating a reference (correct) query $Q_0$ over D. If $Q(D)$ differs from $Q_0(D)$, the user knows Q is incorrect. However, D can be large (often by design), which makes debugging Q difficult. The key idea behind RATest is to show the user a much smaller database instance $D' \subseteq D$, which we call a counterexample, such that $Q(D') \neq Q_0(D')$. RATest builds on data provenance and constraint solving, and employs a suite of techniques to support, at interactive speed, complex queries involving differences and group-by aggregation. We demonstrate an application of RATest in learning: it has been used successfully by a large undergraduate database course in a university to help students with a relational algebra assignment.

https://dl.acm.org/doi/pdf/10.1145/3299869.3320236

RATest: Explaining Wrong Relational Queries Using Small Examples

This paper studies multi-way join queries over temporal data, where each tuple is associated with a valid time interval indicating when the tuple is valid. A temporal join requires that joining tuples' valid intervals intersect. Previous work on temporal joins has focused on joining two relations, but pairwise processing is often inefficient because it may generate unnecessarily large intermediate results. This paper investigates how to efficiently process complex temporal joins involving multiple relations. We also consider a useful extension, durable temporal joins, which further selects results with long enough valid intervals so they are not merely transient patterns. We classify temporal join queries into different classes based on their computational complexity. We identify the class of r-hierarchical joins and show that a linear-time algorithm exists for a temporal join if and only it is r-hierarchical (assuming the 3SUM conjecture holds). We further propose output-sensitive algorithms for non-r-hierarchical joins. We implement our algorithms and evaluate them on both synthetic and real datasets.

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Computing Complex Temporal Join Queries Efficiently

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.

/pdf/subscriber-assignment-for-wide-area-content-based-publish-1bfmdy4c97.pdf

Subscriber assignment for wide-area content-based publish/subscribe

As networks continue to grow in size and complexity, distributed network monitoring and resource querying are becoming increasingly difficult. Our aim is to design, build, and evaluate a scalable infrastructure for answering queries over distributed measurements, at reduced costs (in terms of both network traffic and query latency) while maintaining required precision. In this infrastructure, each network node owns a set of numerical measurements and actively maintains bounds on these values cached at other nodes. We can answer queries approximately, using bounds from nearby caches to avoid contacting the owners directly. We focus on developing efficient and scalable techniques to place, locate, and manage bounded approximate caches across a large network. We have developed two approaches: One uses a recursive partitioning of the network space to place caches in a static, controlled manner, while the other uses a locality-aware distributed hash table to place caches in a dynamic and decentralized manner. In this paper, we focus on the latter approach. Experiments over a large-scale emulated network show that our techniques are very effective in reducing query costs while generating an acceptable amount of background traffic; they are also able to exploit various forms of locality that are naturally present in queries, and adapt to volatility of measurements.

Jun Yang

Papers

From Data Reverence to Data Relevance: Model-Mediated Wireless Sensing of the Physical Environment

RATest: Explaining Wrong Relational Queries Using Small Examples

Computing Complex Temporal Join Queries Efficiently

Subscriber assignment for wide-area content-based publish/subscribe

Distributed network querying with bounded approximate caching