Database Research Community Research Articles

Legal Perspectives on Research Data Storage

Responsibly managing research data has become increasingly important for researchers, especially within the database research community. Despite significant progress in best practices, the state-of-the-art in research data storage is lacking from a legal perspective. We introduce the stakeholders and the dynamic nature of their relationships (such as researchers changing affiliation) and observe that no existing infrastructure for research data storage fully meets their requirements. Therefore, we emphasize the need to design a comprehensive system architecture for research data storage that is aligned with legal considerations from the start, rather than as an afterthought.

Understanding the Performance Implications of the Design Principles in Storage-Disaggregated Databases

Storage-compute disaggregation has recently emerged as a novel architecture in modern data centers, particularly in the cloud. By decoupling compute from storage, this new architecture enables independent and elastic scaling of compute and storage resources, potentially increasing resource utilization and reducing overall costs. To best leverage the disaggregated architecture, a new breed of database systems termed storage-disaggregated databases has recently been developed, such as Amazon Aurora, Microsoft Socrates, Google AlloyDB, Alibaba PolarDB, and Huawei Taurus. However, little is known about the effectiveness of the design principles in these databases since they are typically developed by industry giants, and only the overall performance results are presented without detailing the impact of individual design principles. As a result, many critical research questions remain unclear, such as the performance impact of storage-disaggregation, the log-as-the-database design, shared-storage, and various log-replay methods. In this paper, we investigate the performance implications of the design principles that are widely adopted in storage-disaggregated databases for the first time. As these databases were usually not open-sourced, we have made a significant effort to implement a storage-disaggregated database prototype based on PostgreSQL v13.0. By fully controlling and instrumenting the codebase, we are able to selectively enable and disable individual optimizations and techniques to evaluate their impact on performance in various scenarios. Furthermore, we open-source our storage-disaggregated database prototype for use by the broader database research community, fostering collaboration and innovation in this field.

Ad Hoc Transactions through the Looking Glass: An Empirical Study of Application-Level Transactions in Web Applications

Many transactions in web applications are constructed ad hoc in the application code. For example, developers might explicitly use locking primitives or validation procedures to coordinate critical code fragments. We refer to database operations coordinated by application code as ad hoc transactions . Until now, little is known about them. This paper presents the first comprehensive study on ad hoc transactions. By studying 91 ad hoc transactions among eight popular open-source web applications, we found that (i) every studied application uses ad hoc transactions (up to 16 per application), 71 of which play critical roles; (ii) compared with database transactions, concurrency control of ad hoc transactions is much more flexible; (iii) ad hoc transactions are error-prone—53 of them have correctness issues, and 33 of them are confirmed by developers; and (iv) ad hoc transactions have the potential for improving performance in contentious workloads by utilizing application semantics such as access patterns. Based on these findings, we discuss the implications of ad hoc transactions to the database research community.

Technical Perspective: Query Answers - Fewer is Faster

We often write queries using LIMIT k, indicating that only k answers are to be returned. This feature is present in most query languages, for different data models: SQL, SPARQL, Cypher etc. For example, in a repository of about 250M SPARQL queries, about 15M queries are of this form. Not surprisingly of course, the database research community studied such queries extensively. The dominant setting is this: there is an ordering on tuples that can be returned by a query. Then the answer is limited to the first k tuples in this ordering.

Ad Hoc Transactions: What They Are and Why We Should Care

Many transactions in web applications are constructed ad hoc in the application code. For example, developers might explicitly use locking primitives or validation procedures to coordinate critical code fragments. We refer to database operations coordinated by application code as ad hoc transactions. Until now, little is known about them. This paper presents the first comprehensive study on ad hoc transactions. By studying 91 ad hoc transactions among 8 popular open-source web applications, we find that (i) every studied application uses ad hoc transactions (up to 16 per application), 71 of which play critical roles; (ii) compared with database transactions, concurrency control of ad hoc transactions is much more flexible; (iii) ad hoc transactions are error-prone-53 of them have correctness issues, and 33 of them are confirmed by developers; and (iv) ad hoc transactions have the potential to improve performance in contentious workloads by utilizing application semantics such as access patterns. Finally, implications of ad hoc transactions to the database research community are discussed.

A Case for Graphics-Driven Query Processing

Over the past decade, the database research community has directed considerable attention towards harnessing the power of GPUs in query processing engines. The proposed techniques have primarily focused on devising customized low-level mechanisms that utilize the raw hardware parallelism provided abundantly by GPU compute kernels. In this paper, we advocate a radically different approach - instead of dealing directly with hardware idiosyncrasies, to leverage the well-established graphics pipeline architecture baked into the GPU hardware. A variety of advantages accrue from this high-level abstraction: (a) Extracting the power of GPUs is outsourced to highly-optimized graphics drivers, thereby providing hardware-consciousness for free; (b) Query processing becomes agnostic to changes in GPU architectures (e.g. integrated vs discrete) and vendors, requiring only a change of drivers; (c) Contemporary graphics APIs also support a compute element, facilitating query operator designs that seamlessly straddle the compute and graphics worlds. As a proof of concept of the above vision, we implement here the workhorse Join and GroupBy operators using core graphics primitives. These implementations, based on the Vulkan API, have been evaluated over large benchmark databases on vanilla hybrid computing platforms. The experimental results indicate both substantive performance benefits (typically, around 2X faster) over existing approaches, as well as auto-tuned portability to new hardware platforms.

The World of Graph Databases from An Industry Perspective

Rapidly growing social networks and other graph data have created a high demand for graph technologies in the market. A plethora of graph databases, systems, and solutions have emerged, as a result. On the other hand, graph has long been a well studied area in the database research community. Despite the numerous surveys on various graph research topics, there is a lack of survey on graph technologies from an industry perspective. The purpose of this paper is to provide the research community with an industrial perspective on the graph database landscape, so that graph researcher can better understand the industry trend and the challenges that the industry is facing, and work on solutions to help address these problems.

Cloud data systems

The panel will discuss the research opportunities for the database research community in the context of cloud native data services.

Nearest neighbor classifiers over incomplete information

Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data. However, inconsistency and incomplete information are ubiquitous in real-world datasets, and their impact on ML applications remains elusive. In this paper, we present a formal study of this impact by extending the notion of Certain Answers for Codd tables , which has been explored by the database research community for decades, into the field of machine learning. Specifically, we focus on classification problems and propose the notion of "Certain Predictions" (CP) --- a test data example can be certainly predicted (CP'ed) if all possible classifiers trained on top of all possible worlds induced by the incompleteness of data would yield the same prediction. We study two fundamental CP queries: (Q1) checking query that determines whether a data example can be CP'ed; and (Q2) counting query that computes the number of classifiers that support a particular prediction (i.e., label). Given that general solutions to CP queries are, not surprisingly, hard without assumption over the type of classifier, we further present a case study in the context of nearest neighbor (NN) classifiers, where efficient solutions to CP queries can be developed --- we show that it is possible to answer both queries in linear or polynomial time over exponentially many possible worlds. We demonstrate one example use case of CP in the important application of "data cleaning for machine learning (DC for ML)." We show that our proposed CPClean approach built based on CP can often significantly outperform existing techniques, particularly on datasets with systematic missing values. For example, on 5 datasets with systematic missingness, CPClean (with early termination) closes 100% gap on average by cleaning 36% of dirty data on average, while the best automatic cleaning approach BoostClean can only close 14% gap on average.

Winds from seattle

The database research community has been notably successful in impacting the industry and academia since the invention of the relational model. Examples of innovation in the last decade include columnar storage for data analytic platforms, cloud data services, HTAP systems, and a new generation of data wrangling systems. Despite this success, critical self-assessment by the community and identifying key opportunities for the future is essential if we are to continue the tradition of impactful research. In the Fall of 2018, following a long tradition that dates back to 1988 [4], and five years after the last such meeting [1], a group of approximately thirty database researchers gathered at the University of Washington, Seattle for two days to discuss the opportunities we have as a community for impactful research. A report from that meeting is now available [2]. The discussions in the Seattle meeting focused not just on technical challenges and opportunities but also on topics related to how we organize ourselves as a community. This VLDB panel follows on from a previous discussion at SIGMOD 2020 [3], to provide a forum for the broader database community to review and debate the findings from the Seattle Report on Database Research [2] as well as to identify other challenges, and opportunities that need to be taken into account.

Combating fake news

Fake news is a major threat to global democracy resulting in diminished trust in government, journalism and civil society. The public popularity of social media and social networks has caused a contagion of fake news where conspiracy theories, disinformation and extreme views flourish. Detection and mitigation of fake news is one of the fundamental problems of our times and has attracted widespread attention. While fact checking websites such as snopes, politifact and major companies such as Google, Facebook, and Twitter have taken preliminary steps towards addressing fake news, much more remains to be done. As an interdisciplinary topic, various facets of fake news have been studied by communities as diverse as machine learning, databases, journalism, political science and many more. The objective of this tutorial is two-fold. First, we wish to familiarize the database community with the efforts by other communities on combating fake news. We provide a panoramic view of the state-of-the-art of research on various aspects including detection, propagation, mitigation, and intervention of fake news. Next, we provide a concise and intuitive summary of prior research by the database community and discuss how it could be used to counteract fake news. The tutorial covers research from areas such as data integration, truth discovery and fusion, probabilistic databases, knowledge graphs and crowdsourcing from the lens of fake news. Effective tools for addressing fake news could only be built by leveraging the synergistic relationship between database and other research communities. We hope that our tutorial provides an impetus towards such synthesis of ideas and the creation of new ones.

An in-depth comparison of s-t reliability algorithms over uncertain graphs

Uncertain, or probabilistic, graphs have been increasingly used to represent noisy linked data in many emerging applications, and have recently attracted the attention of the database research community. A fundamental problem on uncertain graphs is the s-t reliability, which measures the probability that a target node t is reachable from a source node s in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Due to the inherent complexity of the s-t reliability estimation problem (#P-hard), various sampling and indexing based efficient algorithms were proposed in the literature. However, since they have not been thoroughly compared with each other, it is not clear whether the later algorithm outperforms the earlier ones. More importantly, the comparison framework, datasets, and metrics were often not consistent (e.g., different convergence criteria were employed to find the optimal number of samples) across these works. We address this serious concern by re-implementing six state-of-the-art s-t reliability estimation methods in a common system and code base, using several medium and large-scale, real-world graph datasets, identical evaluation metrics, and query workloads. Through our systematic and in-depth analysis of experimental results, we report surprising findings, such as many follow-up algorithms can actually be several orders of magnitude inefficient, less accurate, and more memory intensive compared to the ones that were proposed earlier. We conclude by discussing our recommendations on the road ahead.

Monochromatic and bichromatic ranked reverse boolean spatial keyword nearest neighbors search

Recently, Reverse k Nearest Neighbors (RkNN) queries, returning every answer for which the query is one of its k nearest neighbors, have been extensively studied on the database research community. But the RkNN query cannot retrieve spatio-textual objects which are described by their spatial location and a set of keywords. Therefore, researchers proposed a RSTkNN query to find these objects, taking both spatial and textual similarity into consideration. However, the RSTkNN query cannot control the size of answer set and to be sorted according to the degree of influence on the query. In this paper, we propose a new problem Ranked Reverse Boolean Spatial Keyword Nearest Neighbors query called Ranked-RBSKNN query, which considers both spatial similarity and textual relevance, and returns t answers with most degree of influence. We propose a separate index and a hybrid index to process such queries efficiently. Experimental results on different real-world and synthetic datasets show that our approaches achieve better performance.

Efficient Answering of Why-Not Questions in Similar Graph Matching

Answering why-not questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called why-not questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph $q$ and a set of missing ( why-not ) graphs, we aim to modify $q$ into a new query graph $q^*$ such that the missing graphs are included in the new answer set of $q^*$ . We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for $q^*$ . Then, we present a two-phase algorithm to find the new query $q^*$ . In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query $q$ within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for $q^*$ . We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets.

On uncertain graphs modeling and queries

Large-scale, highly-interconnected networks pervade both our society and the natural world around us. Uncertainty, on the other hand, is inherent in the underlying data due to a variety of reasons, such as noisy measurements, lack of precise information needs, inference and prediction models, or explicit manipulation, e.g., for privacy purposes. Therefore, uncertain, or probabilistic, graphs are increasingly used to represent noisy linked data in many emerging application scenarios, and they have recently become a hot topic in the database research community. While many classical graph algorithms such as reachability and shortest path queries become # P -complete, and hence, more expensive in uncertain graphs; various complex queries are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this tutorial, we discuss the sources of uncertain graphs and their applications, uncertainty modeling, as well as the complexities and algorithmic advances on uncertain graphs processing in the context of both classical and emerging graph queries. We emphasize the current challenges and highlight some future research directions.

An efficient scheme for probabilistic skyline queries over distributed uncertain data

Uncertain data has already widely existed in many practical applications, such as sensor networks, RFID networks, location-based services and mobile object management, etc. The skyline queries over uncertain data as an important aspect of uncertain data management, has received extensive attention from the database research community currently, due to its importance in many application including multi-criteria decision making, preference answering, market analysis, etc. However, in most uncertainty applications, the uncertain data are usually collected from vast number of independent data sources among geographically scattered sites, which makes the central assembly of data at one location for storage and query is infeasible and inefficient. Taking account of the network delay and limited bandwidth associated with sharing and communicating large amounts of distributed data over an internet, an important and challenging problem in the scenario is to retrieve all the global skyline tuples from all the distributed local sites with minimum communication cost. In this paper, we propose GFS, which is an efficient scheme for probabilistic skyline over distributed uncertain data. GFS firstly prunes the unqualified tuples with the global grid information and further iteratively prune the unqualified tuples with an improved feedback mechanism. Extensive experiments confirm that the effectiveness and the efficiency of the GFS scheme.

Approaches and Challenges in Database Intrusion Detection

Databases often support enterprise business and store its secrets. This means that securing them from data damage and information leakage is critical. In order to deal with intrusions against database systems, Database Intrusion Detection Systems (DIDS) are frequently used. This paper presents a survey on the main database intrusion detection techniques currently available and discusses the issues concerning their application at the database server layer. The identified weak spots show that most DIDS inadequately deal with many characteristics of specific database systems, such as ad hoc workloads and alert management issues in data warehousing environments, for example. Based on this analysis, research challenges are presented, and requirements and guidelines for the design of new or improved DIDS are proposed. The main finding is that the development and benchmarking of specifically tailored DIDS for the context in which they operate is a relevant issue, and remains a challenge. We trust this work provides a strong incentive to open the discussion between both the security and database research communities.

The Beckman Report on Database Research

Every few years a group of database researchers meets to discuss the state of database research, its impact on practice, and important new directions. This report summarizes the discussion and conclusions of the eighth such meeting, held October 14- 15, 2013 in Irvine, California. It observes that Big Data has now become a defining challenge of our time, and that the database research community is uniquely positioned to address it, with enormous opportunities to make transformative impact. To do so, the report recommends significantly more attention to five research areas: scalable big/fast data infrastructures; coping with diversity in the data management landscape; end-to-end processing and understanding of data; cloud services; and managing the diverse roles of people in the data life cycle.

Aggregate nearest neighbor queries in uncertain graphs

Most recently, uncertain graph data begin attracting significant interests of database research community, because uncertainty is the intrinsic property of the real-world and data are more suitable to be modeled as graphs in numbers of applications, e.g. social network analysis, PPI networks in biology, and road network monitoring. Meanwhile, as one of the basic query operators, aggregate nearest neighbor (ANN) query retrieves a data entity whose aggregate distance, e.g. sum, max, to the given query data entities is smaller than those of other data entities in a database. ANN query on both certain graph data and high dimensional data has been well studied by previous work. However, existing ANN query processing approaches cannot handle the situation of uncertain graphs, because topological structures of an uncertain graph may vary in different possible worlds. Motivated by this, we propose the aggregate nearest neighbor query in uncertain graphs (UG-ANN) in this paper. First of all, we give the formal definition of UG-ANN query and the basic UG-ANN query algorithm. After that, to improve the efficiency of UG-ANN query processing, we develop two kinds of pruning approaches, i.e. structural pruning and instance pruning. The structural pruning takes advantages the monotonicity of the aggregate distance to derive the upper and lower bounds of the aggregate distance for reducing the graph size. Whereas, the instance pruning decreases the number of possible worlds to be checked in the searching tree. Comprehensive experimental results on real-world data sets demonstrate that the proposed method significantly improves the efficiency of the UG-ANN query processing.

Expanding Database Keyword Search for Database Exploration

Database keyword search (DB KWS) has received a lot of attention in database research community. Although much of the research has been motivated by improving performance, recent research has also paid increased attention to its role in database contents exploration or data mining. In this paper we explore aspects related to DB KWS in two steps: First, we expand DB KWS by incorporating ontologies to better capture users’ intention. Furthermore, we examine how KWS or ontology-enriched KWS can offer useful hints for better understanding of the data and in-depth analysis of the data contents, or data mining.