The task of similarity search in multimedia databases is usually accomplished by range or k nearest neighbor queries. However, the expressing power of these "single-example" queries fails when the user's delicate query intent is not available as a single example. Recently, the well-known skyline operator was reused in metric similarity search as a "multi-example" query type. When applied on a multi-dimensional database (i.e., on a multi-attribute table), the traditional skyline operator selects all database objects that are not dominated by other objects. The metric skyline query adopts the skyline operator such that the multiple attributes are represented by distances (similarities) to multiple query examples. Hence, we can view the metric skyline as a set of representative database objects which are as similar to all the examples as possible and, simultaneously, are semantically distinct. In this paper we propose a technique of processing the metric skyline query by use of PM-tree, while we show that our technique significantly outperforms the original M-tree based implementation in both time and space costs. In experiments we also evaluate the partial metric skyline processing, where only a controlled number of skyline objects is retrieved.
Skyline is widely used in reality to solve multi-criteria problems, such as environmental monitoring and business decision-making. When a data is not worse than another data on all criteria and is better than another data at least one criterion, the data is said to dominate another data. When a data item is not dominated by any other data item, this data is said to be a member of the skyline. However, as the number of criteria increases, the possibility that a data dominates another data decreases, resulting in too many members of the skyline set. To solve this kind of problem, the concept of …
Living in the Information Age allows almost everyone have access to a large amount of information and options to choose from in order to fulfill their needs. In many cases, the amount of information available and the rate of change may hide the optimal and truly desired solution. This reveals the need of a mechanism that will highlight the best options to choose among every possible scenario. Based on this the skyline query was proposed which is a decision support mechanism, that retrieves the valuefor- money options of a dataset by identifying the objects that present the optimal combination …
Multi-criteria decision making has been made possible with the advent of skyline queries. However, processing such queries for high dimensional datasets remains a time consuming task. Real-time applications are thus infeasible, especially for non-indexed skyline techniques where the datasets arrive online. In this paper, we propose a caching mechanism that uses the semantics of previous skyline queries to improve the processing time of a new query. In addition to exact queries, utilizing such special semantics allow accelerating related queries. We achieve this by generating partial result set…
The skyline of a set $P$ of points ($SKY(P)$) consists of the "best" points with respect to minimization or maximization of the attribute values. A point $p$ dominates another point $q$ if $p$ is as good as $q$ in all dimensions and it is strictly better than $q$ in at least one dimension. In this work, we focus on the static $2$-d space and provide expected performance guarantees for $3$-sided Range Skyline Queries on the Grid, where $N$ is the cardinality of $P$, $B$ the size of a disk block, and $R$ the capacity of main memory. We present the MLR-tree, which offers optimal expected cost for…
With the advancement of technology, the data generated in our lives is getting faster and faster, and the amount of data that various applications need to process becomes extremely huge. Therefore, we need to put more effort into analyzing data and extracting valuable information. Cloud computing used to be a good technology to solve a large number of data analysis problems. However, in the era of the popularity of the Internet of Things (IoT), transmitting sensing data back to the cloud for centralized data analysis will consume a lot of wireless communication and network transmission costs. …
Skyline queries enable multi-criteria optimization by filtering objects that are worse in all the attributes of interest than another object. To handle the large answer set of skyline queries in high-dimensional datasets, the concept of k-dominance was proposed where an object is said to dominate another object if it is better (or equal) in at least k attributes. This relaxes the full domination criterion of normal skyline queries and, therefore, produces lesser number of skyline objects. This is called the k-dominant skyline set. Many practical applications, however, require that the preferen…
Outsourcing data and computation to cloud server provides a cost-effective way to support large scale data storage and query processing. However, due to security and privacy concerns, sensitive data (e.g., medical records) need to be protected from the cloud server and other unauthorized users. One approach is to outsource encrypted data to the cloud server and have the cloud server perform query processing on the encrypted data only. It remains a challenging task to support various queries over encrypted data in a secure and efficient way such that the cloud server does not gain any knowledge…
The well-known benefits of cloud computing have spurred the popularity of database service outsourcing, where one can resort to the cloud to conveniently store and query databases. Coming with such popular trend is the threat to data privacy, as the cloud gains access to the databases and queries which may contain sensitive information, like medical or financial data. A large body of work has been presented for querying encrypted databases, which has been mostly focused on secure keyword search. In this paper, we instead focus on the support for secure skyline query processing over encrypted o…
It is now cost-effective to outsource large dataset and perform query over the cloud. However, in this scenario, there exist serious security and privacy issues that sensitive information contained in the dataset can be leaked. The most effective way to address that is to encrypt the data before outsourcing. Nevertheless, it remains a grand challenge to process queries in ciphertext efficiently. In this work, we shall focus on solving one representative query task, namely dynamic skyline query, in a secure manner over the cloud. However, it is difficult to be performed on encrypted data as its…
Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top-k and skyline queries have been widely adopted, but they both have inherent drawbacks, i.e., the user either has to provide a specific utility function or faces many results. The k-regret minimization query is proposed, which integrates the merits of top-k and skyline queries. Due to the NP-hardness of the problem, the k-regret minimization query is time consuming and the greedy framework is widely adopted. However, formal theoretical ana…
With the popularity of mobile devices and the development of geo-positioning technology, location-based services (LBS) attract much attention and top-k spatial keyword queries become increasingly complex. It is common to see that clients issue a query to find a restaurant serving pizza and steak, low in price and noise level particularly. However, most of prior works focused only on the spatial keyword while ignoring these independent numerical attributes. In this paper we demonstrate, for the first time, the Attributes-Aware Spatial Keyword Query (ASKQ), and devise a two-layer hybrid index st…
Approximate query processing over dynamic databases, i.e., under insertions/deletions, has applications ranging from high-frequency trading to internet-of-things analytics. We present JanusAQP, a new dynamic AQP system, which supports SUM, COUNT, AVG, MIN, and MAX queries under insertions and deletions to the dataset. JanusAQP extends static partition tree synopses, which are hierarchical aggregations of datasets, into the dynamic setting. This paper contributes new methods for: (1) efficient initialization of the data synopsis in the presence of incoming data, (2) maintenance of the data syno…
Smart city has been consider the wave of the future and the route recommendation in networks is a fundamental problem in it. Most existing approaches for the shortest route problem consider that there is only one kind of cost in networks. However, there always are several kinds of cost in networks and users prefer to select an optimal route under the global consideration of these kinds of cost. In this paper, we study the problem of finding the optimal route in the multi-cost networks. We prove this problem is NP-hard and the existing index techniques cannot be used to this problem. We propose…
The performance of today's in-memory indexes is bottlenecked by the memory latency/bandwidth wall. Processing-in-memory (PIM) is an emerging approach that potentially mitigates this bottleneck, by enabling low-latency memory access whose aggregate memory bandwidth scales with the number of PIM nodes. There is an inherent tension, however, between minimizing inter-node communication and achieving load balance in PIM systems, in the presence of workload skew. This paper presents PIM-tree, an ordered index for PIM systems that achieves both low communication and high load balance, regardless of t…
This paper proposes a client-server decision tree learning method for outsourced private data. The privacy model is anatomization/fragmentation: the server sees data values, but the link between sensitive and identifying information is encrypted with a key known only to clients. Clients have limited processing and storage capability. Both sensitive and identifying information thus are stored on the server. The approach presented also retains most processing at the server, and client-side processing is amortized over predictions made by the clients. Experiments on various datasets show that the…
The cloud infrastructure motivates disaggregation of monolithic data stores into components that are assembled together based on an application's workload. This study investigates disaggregation of an LSM-tree key-value store into components that communicate using RDMA. These components separate storage from processing, enabling processing components to share storage bandwidth and space. The processing components scatter blocks of a file (SSTable) across an arbitrary number of storage components and balance load across them using power-of-d. They construct ranges dynamically at runtime to para…
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
Learned indices have been proposed to replace classic index structures like B-Tree with machine learning (ML) models. They require to replace both the indices and query processing algorithms currently deployed by the databases, and such a radical departure is likely to encounter challenges and obstacles. In contrast, we propose a fundamentally different way of using ML techniques to improve on the query performance of the classic R-Tree without the need of changing its structure or query processing algorithms. Specifically, we develop reinforcement learning (RL) based models to decide how to c…
With the growing use of location-based services, location privacy attracts increasing attention from users, industry, and the research community. While considerable effort has been devoted to inventing techniques that prevent service providers from knowing a user's exact location, relatively little attention has been paid to enabling so-called peer-wise privacy--the protection of a user's location from unauthorized peer users. This paper identifies an important efficiency problem in existing peer-privacy approaches that simply apply a filtering step to identify users that are located in a quer…
We consider the problem of constructing efficient P2P overlays for sensornets providing "Energy-Level Application and Services". The method presented in \cite{SOPXM09} presents a novel P2P overlay for Energy Level discovery in a sensornet. However, this solution is not dynamic, since requires periodical restructuring. In particular, it is not able to support neither join of sensor\_nodes with energy level out of the ranges supported by the existing p2p overlay nor leave of \emph{empty} overlay\_peers to which no sensor\_nodes are currently associated. On this purpose and based on the efficient…
Embedded devices collect and process significant amounts of data in a variety of applications including environmental monitoring, industrial automation and control, and other Internet of Things (IoT) applications. Storing data efficiently is critically important, especially when the device must perform local processing on the data. The most widely used data structure for high performance query and insert is the B-tree. However, existing implementations consume too much memory for small embedded devices and often rely on operating system support. This work presents an extremely memory efficient…
Many modern applications require the evaluation of analytical queries on large amounts of data. Such queries entail joins and heavy aggregations that often include user-defined functions (UDFs). The most efficient way to process these specific type of queries is using tree execution plans. In this work, we develop an engine for analytical query processing and a suite of specialized techniques that collectively take advantage of the tree form of such plans. The engine executes these tree plans in an elastic IaaS cloud infrastructure and dynamically adapts by allocating and releasing pertinent r…
Sample-based approximate query processing (AQP) suffers from many pitfalls such as the inability to answer very selective queries and unreliable confidence intervals when sample sizes are small. Recent research presented an intriguing solution of combining materialized, pre-computed aggregates with sampling for accurate and more reliable AQP. We explore this solution in detail in this work and propose an AQP physical design called PASS, or Precomputation-Assisted Stratified Sampling. PASS builds a tree of partial aggregates that cover different partitions of the dataset. The leaf nodes of this…
In recent years, the Log Structured Merge (LSM) tree has been widely adopted by NoSQL and NewSQL systems for its superior write performance. Despite its popularity, however, most existing work has focused on LSM-based key-value stores with only a primary LSM-tree index; auxiliary structures, which are critical for supporting ad-hoc queries, have received much less attention. In this paper, we focus on efficient data ingestion and query processing for general-purpose LSM-based storage systems. We first propose and evaluate a series of optimizations for efficient batched point lookups, significa…
Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also leads to a huge overhead of the continuous updating during creating the index. An in-memory database usually has a higher query processing performance than disk databases and is more suitable for real-time query processing. Therefore, there is an urgent need to reduce the index creation and update cost for in-memory databases. Database cracking technology is currently recognized as an effective method to reduce the index…
Processing moving object trajectories arises in many application domains and has been addressed by practitioners in the spatiotemporal database and Geographical Information System communities. In this work, we focus on a trajectory similarity search, the distance threshold query, which finds all trajectories within a given distance d of a search trajectory over a time interval. We demonstrate the performance of a multithreaded implementation which features the use of an R-tree index and which has high parallel efficiency (78%-90%). We introduce a GPGPU implementation which avoids the use of in…
