Efficient subspace skyline query based on user preference using MapReduce

Subspace skyline, as an important variant of skyline, has been widely applied for multiple-criteria decisions, business planning. With the development of mobile internet, subspace skyline query in mobile distributed environments has recently attracted considerable attention. However, efficiently obtaining the meaningful subset of skyline points in any subspace remains a challenging task in the current mobile internet. For more and more mobile applications, subspace skyline query on mobile units is usually limited by big data and wireless bandwidth. To address this issue, in this paper, we propose a system model that can support subspace skyline query in mobile distributed environment. An efficient algorithm for processing the Subspace Skyline Query using MapReduce (SSQ) is also presented which can obtain the meaningful subset of points from the full set of skyline points in any subspace. The SSQ algorithm divides a subspace skyline query into two processing phases: the preprocess phase and the query phase. The preprocess phase includes the pruning process and constructing index process which is designed to reduce network delay and response time. Additionally, the query phase provides two filtering methods, SQM-filtering and ε-filtering, to filter the skyline points according to user preference and reduce network cost. Extensive experiments on real and synthetic data are conducted and the experimental results indicate that our algorithm is much efficient, meanwhile, the pruning strategy can further improve the efficiency of the algorithm

Supporting Multi-Criteria Decision Support Queries over Disparate Data Sources

In the era of big data revolution, marked by an exponential growth of information, extracting value from data enables analysts and businesses to address challenging problems such as drug discovery, fraud detection, and earthquake predictions. Multi-Criteria Decision Support (MCDS) queries are at the core of big-data analytics resulting in several classes of MCDS queries such as OLAP, Top-K, Pareto-optimal, and nearest neighbor queries. The intuitive nature of specifying multi-dimensional preferences has made Pareto-optimal queries, also known as skyline queries, popular. Existing skyline algorithms however do not address several crucial issues such as performing skyline evaluation over disparate sources, progressively generating skyline results, or robustly handling workload with multiple skyline over join queries. In this dissertation we thoroughly investigate topics in the area of skyline-aware query evaluation. In this dissertation, we first propose a novel execution framework called SKIN that treats skyline over joins as first class citizens during query processing. This is in contrast to existing techniques that treat skylines as an add-on, loosely integrated with query processing by being placed on top of the query plan. SKIN is effective in exploiting the skyline characteristics of the tuples within individual data sources as well as across disparate sources. This enables SKIN to significantly reduce two primary costs, namely the cost of generating the join results and the cost of skyline comparisons to compute the final results. Second, we address the crucial business need to report results early; as soon as they are being generated so that users can formulate competitive decisions in near real-time. On top of SKIN, we built a progressive query evaluation framework ProgXe to transform the execution of queries involving skyline over joins to become non-blocking, i.e., to be progressively generating results early and often. By exploiting SKIN\u27s principle of processing query at multiple levels of abstraction, ProgXe is able to: (1) extract the output dependencies in the output spaces by analyzing both the input and output space, and (2) exploit this knowledge of abstract-level relationships to guarantee correctness of early output. Third, real-world applications handle query workloads with diverse Quality of Service (QoS) requirements also referred to as contracts. Time sensitive queries, such as fraud detection, require results to progressively output with minimal delay, while ad-hoc and reporting queries can tolerate delay. In this dissertation, by building on the principles of ProgXe we propose the Contract-Aware Query Execution (CAQE) framework to support the open problem of contract driven multi-query processing. CAQE employs an adaptive execution strategy to continuously monitor the run-time satisfaction of queries and aggressively take corrective steps whenever the contracts are not being met. Lastly, to elucidate the portability of the core principle of this dissertation, the reasoning and query processing at different levels of data abstraction, we apply them to solve an orthogonal research question to auto-generate recommendation queries that facilitate users in exploring a complex database system. User queries are often too strict or too broad requiring a frustrating trial-and-error refinement process to meet the desired result cardinality while preserving original query semantics. Based on the principles of SKIN, we propose CAPRI to automatically generate refined queries that: (1) attain the desired cardinality and (2) minimize changes to the original query intentions. In our comprehensive experimental study of each part of this dissertation, we demonstrate the superiority of the proposed strategies over state-of-the-art techniques in both efficiency, as well as resource consumption

Progressive Result Generation for Multi-Criteria Decision Support Queries

Multi-criteria decision support (MCDS) is crucial in many business and web applications such as web searches, B2B portals and on-line commerce. Such MCDS applications need to report results early; as soon as they are being generated so that they can react and formulate competitive decisions in near real-time. The ease in expressing user preferences in web-based applications has made Pareto-optimal (skyline) queries a popular class of MCDS queries. However, state-of-the-art techniques either focus on handling skylines on single input sets (i.e., no joins) or do not tackle the challenge of producing progressive early output results. In this work, we propose a progressive query evaluation framework ProgXe that transforms the execution of queries involving skyline over joins to be non-blocking, i.e., to be progressively generating results early and often. In ProgXe the query processing (join, mapping and skyline) is conducted at multiple levels of abstraction, thereby exploiting the knowledge gained from both input as well as mapped output spaces. This knowledge enables us to identify and reason about abstractlevel relationships to guarantee correctness of early output. It also provides optimization opportunities previously missed by current techniques. To further optimize ProgXe, we incorporate an ordering technique that optimizes the rate at which results are reported by translating the optimization of tuple-level processing into a job-sequencing problem. Our experimental study over a wide variety of data sets demonstrates the superiority of our approach over state-of-the-art techniques

Look Before You Leap: An Adaptive Processing Strategy For Multi-Criteria Decision Support Queries

In recent years, we have witnessed a massive acquisition of data and increasing need to support multi-criteria decision support (MCDS) queries efficiently. Pareto-optimal also known as skyline queries is a popular class of MCDS queries and has received a lot of attention resulting in a flurry of efficient skyline algorithms. The vast majority of such algorithms focus entirely on the input being a single data set. In this work, we provide an adaptive query evaluation technique --- AdaptiveSky that is able to reason at different levels of abstraction thereby effectively minimizing the two primary costs, namely the cost of generating join results and the cost of dominance comparisons to compute the final skyline of the join results. Our approach hinges on two key principles. First, in the input space -- we determine the abstraction levels dynamically at run time instead of assigning a static one at compile time that may or may not work for different data distributions. This is achieved by adaptively partitioning the input data as intermediate results are being generated thereby eliminating the need to access vast majority of the input tuples. Second, we incrementally build the output space, containing the final skyline, without generating a single join result. Our approach is able to reason about the final result space and selectively drill into regions in the output space that show promise in generating result tuples to avoid generation of results that do not contribute to the query result. In this effort, we propose two alternate strategies for reasoning, namely the Euclidean Distance method and the cost-benefit driven Dominance Potential method for reasoning. Our experimental evaluation demonstrates that AdaptiveSky shows superior performance over state-of-the-art techniques over benchmark data sets

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

Efficient Computation of Group Skyline Queries on MapReduce

Skyline query is one of the important issues indatabase research and has been applied in diverse applicationsincluding multi-criteria decision support systems and so on. Theresponse of a skyline query eliminates unnecessary tuples andreturns only the user-interested result. Traditional skyline querypicks out the outstanding tuples, based on one-to-one recordcomparisons. Some modern applications request, beyond thesingular ones, for superior combinations of records. For example,fantasy basketball is composed of 5 players, fantasy baseball of 9players, and a hackathon of several programmers. Group skylineaims at considering all the groups comprising several records,and finding out the non-dominated ones. Because of the highcomplexity, few studies have been conducted and none has beenpresented in either distributed or parallel computing. This paperis the first study that solves the group skyline in the distributedMapReduce framework. We propose the MRGS algorithm togenerate all the combinations, compute the winners at each localnode, and find out the answer globally. We further propose theMRIGS algorithm to release the bottleneck of MRGS onunbalanced computing load of nodes. Finally, we propose theMRIGS-P algorithm to prune the impossible combinations andproduce indexed and balanced MapReduce computation.Extensive experiments with NBA datasets show that MRIGS-P is6 times faster than the MRGS algorithm