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The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.

Relational joins are at the core of relational algebra, which in turn is the core of the standard database query language SQL. As their evaluation is expensive and very often dominated by the output size, it is an important task for database query optimizers to compute estimates on the size of joins and to find good execution plans for sequences of joins. We study these problems from a theoretical perspective, both in the worst-case model and in an average-case model where the database is chosen according to a known probability distribution. In the former case, our first key observation is that the worst-case size of a query is characterized by the fractional edge cover number of its underlying hypergraph, a combinatorial parameter previously known to provide an upper bound. We complete the picture by proving a matching lower bound and by showing that there exist queries for which the join-project plan suggested by the fractional edge cover approach may be substantially better than any join plan that does not use intermediate projections. On the other hand, we show that in the average-case model, every join-project plan can be turned into a plan containing no projections in such a way that the expected time to evaluate the plan increases only by a constant factor independent of the size of the database. Not surprisingly, the key combinatorial parameter in this context is the maximum density of the underlying hypergraph. We show how to make effective use of this parameter to eliminate the projections. [PUBLICATION ABSTRACT]

This research aims to evaluate the quality of the literature search strategy of recently published bibliometric studies in the field of Business and Economics. The search strategy is evaluated from two perspectives, i.e., reporting quality and the ability of search query to find relevant literature. Results showed that selected bibliometric studies did not report their search strategies effectively. Particularly, keyword selection, complete search query and search space are not reported adequately. Results also revealed that the search query quality was not good in most cases, especially in selecting the appropriate synonyms, applying the Boolean operators, and applying the appropriate search space. Further, this research recommended a “crisscross” strategy to extract the relevant literature. It is suggested that future studies can increase the search query quality by adopting the suggested framework.

Uncertain data have already widely existed in many practical applications recently, such as sensor networks, RFID networks, location-based services, and mobile object management. Query processing over uncertain data as an important aspect of uncertain data management has received increasing attention in the field of database. Uncertain query processing poses inherent challenges and demands non-traditional techniques, due to the data uncertainty. This paper surveys this interesting and still evolving research area in current database community, so that readers can easily obtain an overview of the state-of-the-art techniques. We first provide an overview of data uncertainty, including uncertainty types, probability representation models, and sources of probabilities. We next outline the current major types of uncertain queries and summarize the main features of uncertain queries. Particularly, we present and analyze several typical uncertain queries in detail, such as skyline queries, top- queries, nearest-neighbor queries, aggregate queries, join queries, range queries, and threshold queries over uncertain data. Finally, we present many interesting research topics on uncertain queries that have not yet been explored.

Recently, a query, called reverse top-k query, is proposed. The reverse top-k query takes an object as input and retrieves the users whose top-k query results include the object while the top-k query retrieves the top-k matching objects based on the user preference. In business analysis, reverse top-k queries are crucial for evaluating product impact and potential market. However, the reverse top-k query assumes that user’s preference is static. In practice, user preference may change with moods, seasons, economic conditions or other reasons. To overcome this disadvantage, this paper proposes a new reverse top-k query, named as durable reverse top-k query, without limitation of user’s preference being static. The durable reverse top-k query retrieves users who put a given object in the top-k favorite objects most of the time during a given time period. An efficient pruning-based algorithm for the queries with fixed k is proposed in this paper. For the case of k being variable, this paper proposes a pruning-based algorithm with an index to achieve a trade-off between time and space. Experiments on both real and synthetic datasets demonstrate that the proposed algorithms are very efficient.

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from data mining, recommendation system and Internet of Things, to Industry 4.0 framework applications. In mining, specifically, it can be used for the classification of human activities, iterative closest point registration and pattern recognition and has also been helpful for intrusion detection systems and fault detection. Due to the importance of kNN queries, many algorithms have been proposed in the literature, for both static and dynamic data. In this paper, we focus on exact kNN queries and present a comprehensive survey of exact kNN queries. In particular, we study two fundamental types of exact kNN queries: the kNN Search queries and the kNN Join queries. Our survey focuses on exact approaches over high-dimensional data space, which covers 20 kNN Search methods and 9 kNN Join methods. To the best of our knowledge, this is the first work of a comprehensive survey of exact kNN queries over high-dimensional datasets. We specifically categorise the algorithms based on indexing strategies, data and space partitioning strategies, clustering techniques and the computing paradigm. We provide useful insights for the evolution of approaches based on the various categorisation factors, as well as the possibility of further expansion. Lastly, we discuss some open challenges and future research directions.

To bridge the gap between users and data, numerous text-to-SQL systems have been developed that allow users to pose natural language questions over relational databases. Recently, novel text-to-SQL systems are adopting deep learning methods with very promising results. At the same time, several challenges remain open making this area an active and flourishing field of research and development. To make real progress in building text-to-SQL systems, we need to de-mystify what has been done, understand how and when each approach can be used, and, finally, identify the research challenges ahead of us. The purpose of this survey is to present a detailed taxonomy of neural text-to-SQL systems that will enable a deeper study of all the parts of such a system. This taxonomy will allow us to make a better comparison between different approaches, as well as highlight specific challenges in each step of the process, thus enabling researchers to better strategise their quest towards the “holy grail” of database accessibility.

The success of the use of ontology-based systems depends on efficient and user-friendly methods of formulating queries against the ontology. We propose a method to query a class of ontologies, called facet ontologies ( fac-ontologies ), using a faceted human-oriented approach. A fac-ontology has two important features: (a) a hierarchical view of it can be defined as a nested facet over this ontology and the view can be used as a faceted interface to create queries and to explore the ontology; (b) the ontology can be converted into an ontological database , the ABox of which is stored in a database, and the faceted queries are evaluated against this database. We show that the proposed faceted interface makes it possible to formulate queries that are semantically equivalent to SROIQ Fac , a limited version of the SROIQ description logic. The TBox of a fac-ontology is divided into a set of rules defining intensional predicates and a set of constraint rules to be satisfied by the database. We identify a class of so-called reflexive weak cycles in a set of constraint rules and propose a method to deal with them in the chase procedure. The considerations are illustrated with solutions implemented in the DAFO system ( data access based on faceted queries over ontologies ).

There are now over 20 commercial vector database management systems (VDBMSs), all produced within the past five years. But embedding-based retrieval has been studied for over ten years, and similarity search a staggering half century and more. Driving this shift from algorithms to systems are new data intensive applications, notably large language models, that demand vast stores of unstructured data coupled with reliable, secure, fast, and scalable query processing capability. A variety of new data management techniques now exist for addressing these needs, however there is no comprehensive survey to thoroughly review these techniques and systems. We start by identifying five main obstacles to vector data management, namely the ambiguity of semantic similarity, large size of vectors, high cost of similarity comparison, lack of structural properties that can be used for indexing, and difficulty of efficiently answering “hybrid” queries that jointly search both attributes and vectors. Overcoming these obstacles has led to new approaches to query processing, storage and indexing, and query optimization and execution. For query processing, a variety of similarity scores and query types are now well understood; for storage and indexing, techniques include vector compression, namely quantization, and partitioning techniques based on randomization, learned partitioning, and “navigable” partitioning; for query optimization and execution, we describe new operators for hybrid queries, as well as techniques for plan enumeration, plan selection, distributed query processing, data manipulation queries, and hardware accelerated query execution. These techniques lead to a variety of VDBMSs across a spectrum of design and runtime characteristics, including “native” systems that are specialized for vectors and “extended” systems that incorporate vector capabilities into existing systems. We then discuss benchmarks, and finally outline research challenges and point the direction for future work.

Around-view multi-camera 3D object detection in BEV (Bird’s-Eye-View) space has been a research focus over the past few years. As a typical supervised training task, many researchers promote this area with different task-specific key designs, such as exploiting temporal information and correspondence of perspective image plane and BEV space. Most of these works follow the DETR detection framework, yet the nature of learnable queries in DETR, the encodings of objects’ center and bounding box information, have not been discussed in previous studies. In this paper, we take advantage of this prior and further extend it to 3D detection tasks. In 3D object detection, the ground-truth bounding boxes are hardly overlapping. Therefore, the queries should be more diverse under this hypothesis. To achieve this goal, we propose a Plug-in Discrimination Module (PDM) to discriminate learnable queries from all the other queries with a discrimination loss to ensure the diversity of queries. The PDM is a simple train-time-only module. It contains a query projection head to project all the object queries into a common latent space. In the latent space, the discrimination loss is conducted on all the queries. Experimental results show that this design can directly improve the 3D detector’s performance without modifying the detector’s architecture and adding extra inference costs. The NDS improvement on the nuScenes dataset is up to a maximum of 1.62% in the 8th training epoch and remains an average 0.64% improvement in the following epochs, compared with the baseline model.