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

/pdf/computational-journalism-a-call-to-arms-to-database-1vo56hcc7u.pdf

Computational Journalism: A Call to Arms to Database Researchers

Large-scale data analytics using statistical machine learning (ML), popularly called advanced analytics, underpins many modern data-driven applications. The data management community has been working for over a decade on tackling data management-related challenges that arise in ML workloads, and has built several systems for advanced analytics. This tutorial provides a comprehensive review of such systems and analyzes key data management challenges and techniques. We focus on three complementary lines of work: (1) integrating ML algorithms and languages with existing data systems such as RDBMSs, (2) adapting data management-inspired techniques such as query optimization, partitioning, and compression to new systems that target ML workloads, and (3) combining data management and ML ideas to build systems that improve ML lifecycle-related tasks. Finally, we identify key open data management challenges for future research in this important area.

Data Management in Machine Learning: Challenges, Techniques, and Systems

Data warehouses collect data into materialized views for analysis. After some time, some of the data may no longer be needed or may not be of interest. In this paper, we handle this by expiring or removing unneeded materialized view tuples. A framework supporting such expiration is presented. Within it, a user or administrator can declaratively request expirations and can specify what type of modifications are expected from external sources. The latter can significantly increase the amount of data that can be expired. We present efficient algorithms for determining what data can be expired (data not needed for maintenance of other views), taking into account the types of updates that may occur

/pdf/expiring-data-in-a-warehouse-2ilq2rso58.pdf

Expiring Data in a Warehouse

Monitoring extreme values (MAX or MIN) is a fundamental problem in wireless sensor networks (and in general, complex dynamic systems). This problem presents very different algorithmic challenges from aggregate and selection queries, in the sense that an individual node cannot by itself determine its inclusion in the query result. We present novel query processing algorithms for this problem, with the goal of minimizing message traffic in the network. These algorithms employ a hierarchy of local constraints, or thresholds, to leverage network topology such that message-passing is localized. We evaluate all algorithms using simulated and real-world data to study various trade-offs.

/pdf/energy-efficient-monitoring-of-extreme-values-in-sensor-16izytl6q8.pdf

Energy-efficient monitoring of extreme values in sensor networks

XML and other types of semistructured data are typically represented by a labeled directed graph. To speed up path expression queries over the graph, a variety of structural indexes have been proposed. They usually work by partitioning nodes in the data graph into equivalence classes and storing equivalence classes as index nodes. A(k)-index introduces the concept of local bisimilarity for partitioning, allowing the trade-off between index size and query answering power. However, all index nodes in A(k)-index have the same local similarity k, which cannot take advantage of the fact that a workload may contain path expressions of different lengths, or that different parts of the data graph may have different local similarity requirements. To overcome these limitations, we propose M(k)- and M*(k)-indexes. The basic M(k)-index is workload-aware: Like the previously proposed D(k)-index, it allows different index nodes to have different local similarity requirements, providing finer partitioning only for parts of the data graph targeted by longer path expressions. Unlike D(k)-index, M(k)-index is never over-refined for irrelevant index or data nodes. However, the workload-aware feature still incurs overrefinement due to over-qualified parent index nodes. Moreover, fine partitions penalize the performance of short path expressions. To solve these problems, we further propose the M*(k)-index. An M*(k)-index consists of a collection of indexes whose nodes are organized in a partition hierarchy, allowing successively coarser partitioning information to co-exist with the finest partitioning information required. Experiments show that our indexes are superior to previously proposed indexes in terms of index size and query performance.

/pdf/multiresolution-indexing-of-xml-for-frequent-queries-2hyulhgq21.pdf

Jun Yang

Papers

Computational Journalism: A Call to Arms to Database Researchers

Data Management in Machine Learning: Challenges, Techniques, and Systems

Expiring Data in a Warehouse

Energy-efficient monitoring of extreme values in sensor networks

Multiresolution indexing of XML for frequent queries