Scalable package queries in relational database systems

Brucato, Matteo; Beltrán, Juan Felipe; Abouzied, Azza; Meliou, Alexandra

doi:10.14778/2904483.2904489

Cited by 23 publications

(24 citation statements)

References 24 publications

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“…The relational data model, and SQL therein, adopt set based constraints that are imposed to each individual tuple in order to appear in a query result set. There are nevertheless many practical real-world problems such as geometric pattern queries that require tuples in a set to collectively satisfy a set of constraints [4], [5]. The former is concerned with topological constraints among multi-dimensional objects.…”

Section: Related Workmentioning

confidence: 99%

“…The former is concerned with topological constraints among multi-dimensional objects. In [5], the authors present package queries that enable users to express constraints over package of tuples. The approach considers local constraints, as traditional where clause in SQL, and global constraint that refer to packages of tuples.…”

Section: Related Workmentioning

confidence: 99%

“…From a constellation query representing the EC, involving a set of sky objects,we should compare it's attributes with other set of sky objects in a astronomy catalog. The data scheme of an astronomy catalog such as Sloan Digital Sky Survey (SDSS) 5 is as the following relation: SDSS (Obj_ID, RA, DEC, u, g, r, i, z, Redshift, . .…”

Section: Introductionmentioning

confidence: 99%

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Pre-processing and Indexing Techniques for Constellation Queries in Big Data

Khatibi¹,

Porto²,

Rittmeyer³

et al. 2017

Big Data Analytics and Knowledge Discovery

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Geometric patterns are defined by a spatial distribution of a set of objects. They can be found in many spatial datasets as in seismic, astronomy, and transportation. A particular interesting geometric pattern is exhibited by the Einstein cross, which is an astronomical phenomenon in which a single quasar is observed as four distinct sky objects when captured by earth telescopes. Finding such crosses, as well as other geometric patterns, collectively referred to as constellation queries, is a challenging problem as the potential number of sets of elements that compose shapes is exponentially large in the size of the dataset and the query pattern. In this paper we propose algorithms to optimize the computation of constellation queries. Our techniques involve pre-processing the query to reduce its dimensionality as well as indexing the data to fasten stars neighboring computation using a PH-tree. We have implemented our techniques in Spark and evaluated our techniques by a series of experiments. The PH-tree indexing showed very good results and guarantees query answer completeness.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Pre-processing and Indexing Techniques for Constellation Queries in Big Data

Khatibi¹,

Porto²,

Rittmeyer³

et al. 2017

Big Data Analytics and Knowledge Discovery

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show abstract

“…Based on a cost-benefit model, a heuristic best-first algorithm is presented in [58] and further optimized by an adaptive prefetching technique to provide swift online results. Along the same lines, [12] presented the Package Query approach which assists users to find a set of tuples that satisfy global constraints (i.e., aggregate constraints) defined and efficient version is also proposed which relies on applying the ILP solvers on a representative set of the data to find approximated solutions, such that these solutions are guaranteed to be close by a factor to the baseline solutions.…”

Section: Similarity-aware Aggregate-based Query Refinementmentioning

confidence: 99%

“…Although the above techniques can be applied to solve the AQR problem efficiently, they exhibit various limitations. Foremost, similarity to users' input queries is completely neglected in the techniques proposed in [14,58,12,79]. Going back to Example 1.1, applying any of the above techniques will help the scientist to achieve her goal, i.e., an optimal refined query R * which optimally satisfies the cardinality constraint.…”

Section: Similarity-aware Aggregate-based Query Refinementmentioning

confidence: 99%

Similarity-aware query refinement for data exploration

Albarrak¹

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Database users are easily overwhelmed by the sheer size of data found in large-scale scientific and financial databases. Exploring these databases to make sense of the explored data and to discover interesting insights (i.e., data exploration) has been, and still is, a hideous and labour-intensive task, especially for non-expert users with no solid background of the underlying data. Some three decades ago, the database research community noticed the limitation of traditional DBMS in supporting users for data exploration tasks. Since then, the research community has proposed and designed various effective and efficient data exploration techniques to assist users in extracting interesting insights from their data. An instance of these techniques is the Query Refinement technique. In query refinement techniques, users' queries are assumed to be imprecise, i.e., the returned result does not meet some user-defined constraints. Accordingly, the goal of query refinement techniques is to automatically refine these imprecise queries to maximize users' satisfaction with the results. In particular, the predicates of the queries are carefully modified so that the returned results satisfy the user-defined constraints. Since users' constraints on the queries results are diverse and miscellaneous, this thesis focuses on two specific forms of constraints in exploring relational and sequential data, namely, 1) user-defined aggregate constraints on the result, and 2) user-defined correlation constraints of time series data. These constraints are common in real world applications because they represent an upper level view of the result that is easier to understand and digest than the raw result itself. This thesis addresses the limitations of current query refinement techniques that are oblivious to the similarity of the refined queries to the users' initial queries. Specifically, users' initial (and imprecise) queries are defined as anchor points for which the similarity of its corresponding refined queries are computed over the whole refinement space. Consequently, the similarity-aware query refinement problem is formulated as a search problem, which aims to balance the trade-off between minimizing the deviation from satisfying a constraint on the query result, and maximizing the similarity of the refined query to the initial one. Searching for a trade-off between satisfying a constraint on the result of a query and maximizing the similarity introduces various challenges. A common challenge shared by many query refinement problems is that finding an optimal trade-off i Lastly, I would like to formally thank my sponsor, Al-Imam Muhammad Ibn Saud Islamic University, for providing the financial support which made this journey possible.

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Relational Expressions for Data Transformation and Computation

Pratten,

Mathieson

2023

Lecture Notes in Computer Science

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Scalable package queries in relational database systems

Cited by 23 publications

References 24 publications

Pre-processing and Indexing Techniques for Constellation Queries in Big Data

Pre-processing and Indexing Techniques for Constellation Queries in Big Data

Similarity-aware query refinement for data exploration

Relational Expressions for Data Transformation and Computation

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