Efficient Computation of Subspace Skyline over Categorical Domains

Platforms such as AirBnB, Zillow, Yelp, and related sites have transformed the way we search for accommodation, restaurants, etc. The underlying datasets in such applications have numerous attributes that are mostly Boolean or Categorical. Discovering the skyline of such datasets over a subset of attributes would identify entries that stand out while enabling numerous applications. There are only a few algorithms designed to compute the skyline over categorical attributes, yet are applicable only when the number of attributes is small. In this paper, we place the problem of skyline discovery over categorical attributes into perspective and design efficient algorithms for two cases. (i) In the absence of indices, we propose two algorithms, ST-S and ST-P, that exploits the categorical characteristics of the datasets, organizing tuples in a tree data structure, supporting efficient dominance tests over the candidate set. (ii) We then consider the existence of widely used precomputed sorted lists. After discussing several approaches, and studying their limitations, we propose TA-SKY, a novel threshold style algorithm that utilizes sorted lists. Moreover, we further optimize TA-SKY and explore its progressive nature, making it suitable for applications with strict interactive requirements. In addition to the extensive theoretical analysis of the proposed algorithms, we conduct a comprehensive experimental evaluation of the combination of real (including the entire AirBnB data collection) and synthetic datasets to study the practicality of the proposed algorithms. The results showcase the superior performance of our techniques, outperforming applicable approaches by orders of magnitude

Scalable parallelization of skyline computation for multi-core processors

The skyline is an important query operator for multi-criteria decision making. It reduces a dataset to only those points that offer optimal trade-offs of dimensions. In general, it is very expensive to compute. Recently, multicore CPU algorithms have been proposed to accelerate the computation of the skyline. However, they do not sufficiently minimize dominance tests and so are not competitive with state-of-the-art sequential algorithms. In this paper, we introduce a novel multicore skyline algorithm, Hybrid, which processes points in blocks. It maintains a shared, global skyline among all threads, which is used to minimize dominance tests while maintaining high throughput. The algorithm uses an efficiently-updatable data structure over the shared, global skyline, based on point-based partitioning. Also, we release a large benchmark of optimized skyline algorithms, with which we demonstrate on challenging workloads a 100-fold speedup over state-of-the-art multicore algorithms and a 10-fold speedup with 16 cores over state-of-the-art sequential algorithms

SkyFlow: heterogeneous streaming for skyline computation using FlowGraph and SYCL

The skyline is an optimization operator widely used for multi-criteria decision making. It allows minimizing an n-dimensional dataset into its smallest subset. In this work we present SkyFlow, the first heterogeneous CPU+GPU graph-based engine for skyline computation on a stream of data queries. Two data flow approaches, Coarse-grained and Fine-grained, have been proposed for different streaming scenarios. Coarse-grained aims to keep in parallel the computation of two queries using a hybrid solution with two state-of-the-art skyline algorithms: one optimized for CPU and another for GPU. We also propose a model to estimate at runtime the computation time of any arriving data query. This estimation is used by a heuristic to schedule the data query on the device queue in which it will finish earlier. On the other hand, Fine-grained splits one query computation between CPU and GPU. An experimental evaluation using as target architecture a heterogeneous system comprised of a multicore CPU and an integrated GPU for different streaming scenarios and datasets, reveals that our heterogeneous CPU+GPU approaches always outperform previous only-CPU and only-GPU state-of-the-art implementations up to 6.86×and 5.19×, respectively, and they fall below 6% of ideal peak performance at most. We also evaluate Coarse-grained vs Fine-Grained finding that each approach is better suited to different streaming scenarios.This work was partially supported by the Spanish projects PID2019-105396RB-I00, UMA18-FEDERJA-108 and P20-00395-R. // Funding for open access charge: Universidad de Málaga / CBUA

A New Efficient Privacy For A Multi-Skyline Queries With Mapreduce

The skyline query technology has pulled in much consideration as of late. This is for the most part because of the significance of horizon brings about numerous applications, for example, multi-criteria basic leadership, information mining, and data suggested frameworks. The horizon administrator has pulled in impressive consideration as of late because of its wide applications. Be that as it may, registering a horizon is testing today since we need to manage enormous information. For information escalated applications, the MapReduce system has been broadly utilized as of late. In this paper, what's more, we apply the strength control sifting technique to viably prune non-horizon focuses ahead of time. We next segment information in light of the districts separated by the quad tree and figure hopeful horizon focuses for each segment utilizing MapReduce. At long last, we propose a productive technique for preparing multi-horizon questions with MapReduce with no change of the Hadoop internals. Through different analyses, we demonstrate that our approach beats past investigations by requests of exten

Complex Query Operators on Modern Parallel Architectures

Identifying interesting objects from a large data collection is a fundamental problem for multi-criteria decision making applications.In Relational Database Management Systems (RDBMS), the most popular complex query operators used to solve this type of problem are the Top-K selection operator and the Skyline operator.Top-K selection is tasked with retrieving the k-highest ranking tuples from a given relation, as determined by a user-defined aggregation function.Skyline selection retrieves those tuples with attributes offering (pareto) optimal trade-offs in a given relation.Efficient Top-K query processing entails minimizing tuple evaluations by utilizing elaborate processing schemes combined with sophisticated data structures that enable early termination.Skyline query evaluation involves supporting processing strategies which are geared towards early termination and incomparable tuple pruning.The rapid increase in memory capacity and decreasing costs have been the main drivers behind the development of main-memory database systems.Although the act of migrating query processing in-memory has created many opportunities to improve the associated query latency, attaining such improvements has been very challenging due to the growing gap between processor and main memory speeds.Addressing this limitation has been made easier by the rapid proliferation of multi-core and many-core architectures.However, their utilization in real systems has been hindered by the lack of suitable parallel algorithms that focus on algorithmic efficiency.In this thesis, we study in depth the Top-K and Skyline selection operators, in the context of emerging parallel architectures.Our ultimate goal is to provide practical guidelines for developing work-efficient algorithms suitable for parallel main memory processing.We concentrate on multi-core (CPU), many-core (GPU), and processing-in-memory architectures (PIM), developing solutions optimized for high throughout and low latency.The first part of this thesis focuses on Top-K selection, presenting the specific details of early termination algorithms that we developed specifically for parallel architectures and various types of accelerators (i.e. GPU, PIM).The second part of this thesis, concentrates on Skyline selection and the development of a massively parallel load balanced algorithm for PIM architectures.Our work consolidates performance results across different parallel architectures using synthetic and real data on variable query parameters and distributions for both of the aforementioned problems.The experimental results demonstrate several orders of magnitude better throughput and query latency, thus validating the effectiveness of our proposed solutions for the Top-K and Skyline selection operators

Optimizing skyline query processing in incomplete data

Given the significance of skyline queries, they are incorporated in various modern applications including personalized recommendation systems as well as decision-making and decision-support systems. Skyline queries are used to identify superior data items in the database. Most of the previously proposed skyline algorithms work on a complete database where the data are always present (non-missing). However, in many contemporary real-world databases, particularly those databases with large cardinality and high dimensionality, such assumption is not necessarily valid. Hence, missing data pose new challenges if the processing skyline queries cannot easily apply those methods that are designed for complete data. This is due to the fact that imperfect data cause the loss of the transitivity property of the skyline method and cyclic dominance. This paper presents a framework called Optimized Incomplete Skyline (OIS) which utilizes a technique that simplifies the skyline process on a database with missing data and helps prune the data items before performing the skyline process. The proposed strategy assures that the number of the domination tests is significantly reduced. A set of experiments has been accomplished using both real and synthetic datasets aimed at validating the performance of the framework. The experiment results confirm that the OIS framework is indeed superior and steadily outperforms the current approaches in terms of the number of domination tests required to retrieve the skylines

Outlier Detection In Big Data

The dissertation focuses on scaling outlier detection to work both on huge static as well as on dynamic streaming datasets. Outliers are patterns in the data that do not conform to the expected behavior. Outlier detection techniques are broadly applied in applications ranging from credit fraud prevention, network intrusion detection to stock investment tactical planning. For such mission critical applications, a timely response often is of paramount importance. Yet the processing of outlier detection requests is of high algorithmic complexity and resource consuming. In this dissertation we investigate the challenges of detecting outliers in big data -- in particular caused by the high velocity of streaming data, the big volume of static data and the large cardinality of the input parameter space for tuning outlier mining algorithms. Effective optimization techniques are proposed to assure the responsiveness of outlier detection in big data. In this dissertation we first propose a novel optimization framework called LEAP to continuously detect outliers over data streams. The continuous discovery of outliers is critical for a large range of online applications that monitor high volume continuously evolving streaming data. LEAP encompasses two general optimization principles that utilize the rarity of the outliers and the temporal priority relationships among stream data points. Leveraging these two principles LEAP not only is able to continuously deliver outliers with respect to a set of popular outlier models, but also provides near real-time support for processing powerful outlier analytics workloads composed of large numbers of outlier mining requests with various parameter settings. Second, we develop a distributed approach to efficiently detect outliers over massive-scale static data sets. In this big data era, as the volume of the data advances to new levels, the power of distributed compute clusters must be employed to detect outliers in a short turnaround time. In this research, our approach optimizes key factors determining the efficiency of distributed data analytics, namely, communication costs and load balancing. In particular we prove the traditional frequency-based load balancing assumption is not effective. We thus design a novel cost-driven data partitioning strategy that achieves load balancing. Furthermore, we abandon the traditional one detection algorithm for all compute nodes approach and instead propose a novel multi-tactic methodology which adaptively selects the most appropriate algorithm for each node based on the characteristics of the data partition assigned to it. Third, traditional outlier detection systems process each individual outlier detection request instantiated with a particular parameter setting one at a time. This is not only prohibitively time-consuming for large datasets, but also tedious for analysts as they explore the data to hone in on the most appropriate parameter setting or on the desired results. We thus design an interactive outlier exploration paradigm that is not only able to answer traditional outlier detection requests in near real-time, but also offers innovative outlier analytics tools to assist analysts to quickly extract, interpret and understand the outliers of interest. Our experimental studies including performance evaluation and user studies conducted on real world datasets including stock, sensor, moving object, and Geolocation datasets confirm both the effectiveness and efficiency of the proposed approaches