141 research outputs found
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
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
Incremental Discovery of Prominent Situational Facts
We study the novel problem of finding new, prominent situational facts, which
are emerging statements about objects that stand out within certain contexts.
Many such facts are newsworthy---e.g., an athlete's outstanding performance in
a game, or a viral video's impressive popularity. Effective and efficient
identification of these facts assists journalists in reporting, one of the main
goals of computational journalism. Technically, we consider an ever-growing
table of objects with dimension and measure attributes. A situational fact is a
"contextual" skyline tuple that stands out against historical tuples in a
context, specified by a conjunctive constraint involving dimension attributes,
when a set of measure attributes are compared. New tuples are constantly added
to the table, reflecting events happening in the real world. Our goal is to
discover constraint-measure pairs that qualify a new tuple as a contextual
skyline tuple, and discover them quickly before the event becomes yesterday's
news. A brute-force approach requires exhaustive comparison with every tuple,
under every constraint, and in every measure subspace. We design algorithms in
response to these challenges using three corresponding ideas---tuple reduction,
constraint pruning, and sharing computation across measure subspaces. We also
adopt a simple prominence measure to rank the discovered facts when they are
numerous. Experiments over two real datasets validate the effectiveness and
efficiency of our techniques
Towards a semantic and statistical selection of association rules
The increasing growth of databases raises an urgent need for more accurate
methods to better understand the stored data. In this scope, association rules
were extensively used for the analysis and the comprehension of huge amounts of
data. However, the number of generated rules is too large to be efficiently
analyzed and explored in any further process. Association rules selection is a
classical topic to address this issue, yet, new innovated approaches are
required in order to provide help to decision makers. Hence, many interesting-
ness measures have been defined to statistically evaluate and filter the
association rules. However, these measures present two major problems. On the
one hand, they do not allow eliminating irrelevant rules, on the other hand,
their abun- dance leads to the heterogeneity of the evaluation results which
leads to confusion in decision making. In this paper, we propose a two-winged
approach to select statistically in- teresting and semantically incomparable
rules. Our statis- tical selection helps discovering interesting association
rules without favoring or excluding any measure. The semantic comparability
helps to decide if the considered association rules are semantically related
i.e comparable. The outcomes of our experiments on real datasets show promising
results in terms of reduction in the number of rules
Contributions à l’Optimisation de Requêtes Multidimensionnelles
Analyser les données consiste à choisir un sous-ensemble des dimensions qui les décriventafin d'en extraire des informations utiles. Or, il est rare que l'on connaisse a priori les dimensions"intéressantes". L'analyse se transforme alors en une activité exploratoire où chaque passe traduit par une requête. Ainsi, il devient primordiale de proposer des solutions d'optimisationde requêtes qui ont une vision globale du processus plutôt que de chercher à optimiser chaque requêteindépendamment les unes des autres. Nous présentons nos contributions dans le cadre de cette approcheexploratoire en nous focalisant sur trois types de requêtes: (i) le calcul de bordures,(ii) les requêtes dites OLAP (On Line Analytical Processing) dans les cubes de données et (iii) les requêtesde préférence type skyline
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
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