Data wrangling | EPFL Graph Search

Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data. Data analysts typically spend the majority of their time in the process of data wrangling compared to the actual analysis of the data. The process of data wrangling may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data wrangling typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data (e.g. sorting) or parsing the data into predefined data structures, and finally depositing the resulting content into a data sink for storage and future use. It is closely aligned with the ETL process. Background

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Styliani Asimina Giannakopoulou

Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. Existing solutions that attempt to automate the data cleaning procedure treat data cleaning as a separate offline process that takes place before analysis begins, while also focusing on a specific use-case. In addition, when the analysis involves complex non-relational data such as graphs, data cleaning becomes more challenging as it involves expensive operations. Therefore, offline, specialized cleaning tools exhibit long running times or fail to process large datasets. At the same time, applying data cleaning before analysis starts assumes a priori knowledge of the inconsistencies and the query workload, thereby requiring effort on understanding and cleaning data that is unnecessary for the analysis. Therefore, from a user's perspective, one is forced to use a different, potentially inefficient tool for each category of errors.In this thesis we aim for coverage and efficiency of data cleaning. We design and build data cleaning systems that employ high-level abstractions to (a) represent and optimize different cleaning operations for data of various formats, and (b) allow for real-time data cleaning that is relevant to data analysis.We introduce CleanM, a language that can express multiple types of cleaning operations. CleanM goes through a three-level translation process for optimization purposes; a different family of optimizations is applied in each abstraction level. Thus, CleanM can express complex data cleaning tasks, optimize them in a unified way, and deploy them in a scaleout fashion.To further reduce the data-to-insight time, we propose an approach that performs probabilistic repair of denial constraint violations on-demand, driven by the exploratory analysis that users perform. We introduce Daisy, a system that seamlessly integrates data cleaning into the analysis by relaxing query results. Daisy executes analytical query-workloads over dirty data by weaving cleaning operators into the query plan.To cover complex data such as graphs, we optimize the building block of data cleaning operations on graph data, that is subgraph matching. Subgraph matching constitutes the main bottleneck when cleaning graph data as it is an NP-complete problem. To optimize subgraph matching, we present a scale-up, radix-based algorithm that starts from an arbitrary partitioning of the graph and coordinates parallel pattern matching to eliminate redundant work among the workers. To address load imbalance, we employ a work stealing technique, specific to the subgraph matching problem. Worker threads steal work from straggler threads by using a heuristic that maximizes the work stolen, while at the same time preserves the order of evaluating candidate vertices.Overall, instead of using offline, specialised tools, this thesis designs abstractions that optimize different cleaning primitives over heterogeneous data, while also integrating data cleaning tasks seamlessly into data analysis. Specifically, we provide (a) a declarative high-level interface backed by an optimizable query calculus and (b) the required optimizations at the underlying data cleaning layers while also taking into consideration the exploratory analysis that users perform.

EPFL2021

Efficient Online Processing for Advanced Analytics

Mohamed Elsayed Mohamed Ahmed El Seidy

With the advent of emerging technologies and the Internet of Things, the importance of online data analytics has become more pronounced. Businesses and companies are adopting approaches that provide responsive analytics to stay competitive in the global marketplace. Online analytics allow data analysts to promptly react to patterns or to gain preliminary insights from early results that aid in research, decision making, and effective strategy planning. The growth of data-velocity in a variety of domains including, high-frequency trading, social networks, infrastructure monitoring, and advertising require adopting online engines that can efficiently process continuous streams of data. This thesis presents foundations, techniques, and systems' design that extend the state-of-the-art in online query processing to efficiently support relational joins with arbitrary join-predicates (beyond traditional equi-joins); and to support other data models (beyond relational) that target machine learning and graph computations. The thesis is divided into two parts: We first present a brief overview of Squall, our open-source online query processing engine that supports SQL-like queries on top of streams. Then, we focus on extending Squall to support efficient theta-join processing. Scalable distributed join processing requires a partitioning policy that evenly distributes the processing load while minimizing the size of maintained state and duplicated messages. Efficient load-balance demands apriori-statistics which are not available in the online setting. We propose a novel operator that continuously adjusts itself to the data dynamics, through adaptive dataflow routing and state repartitioning. It is also resilient to data-skew, maintains high throughput rates, avoids blocking during state repartitioning, and behaves as a black-box dataflow operator with provable performance guarantees. Our evaluation demonstrates that the proposed operator outperforms the state-of-the-art static partitioning schemes in resource utilization, throughput, and execution time up to 7x. In the second part, we present a novel framework that supports the Incremental View Maintenance (IVM) of workloads expressed as linear algebra programs. Linear algebra represents a concrete substrate for advanced analytical tasks including, machine learning, scientific computation, and graph algorithms. Previous works on relational calculus IVM are not applicable to matrix algebra workloads. This is because a single entry change to an input-matrix results in changes all over the intermediate views, rendering IVM useless in comparison to re-evaluation. We present Lago, a unified modular compiler framework that supports the IVM of a broad class of linear algebra programs. Lago automatically derives and optimizes incremental trigger programs of analytical computations, while freeing the user from erroneous manual derivations, low-level implementation details, and performance tuning. We present a novel technique that captures

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changes as low-rank matrices. Low-rank matrices are representable in a compressed factored form that enables cheaper computations. Lago automatically propagates the factored representation across program statements to derive an efficient trigger program. Moreover, Lago extends its support to other domains that use different semi-ring configurations, e.g., graph applications. Our evaluation results demonstrate orders of magnitude (10x-10

EPFL2017