Maik Thiele

Bio: Maik Thiele is an academic researcher from Dresden University of Technology. The author has contributed to research in topics: Data warehouse & Graph database. The author has an hindex of 14, co-authored 80 publications receiving 726 citations.

Cardinality estimation with local deep learning models

Abstract: Cardinality estimation is a fundamental task in database query processing and optimization. Unfortunately, the accuracy of traditional estimation techniques is poor resulting in non-optimal query execution plans. With the recent expansion of machine learning into the field of data management, there is the general notion that data analysis, especially neural networks, can lead to better estimation accuracy. Up to now, all proposed neural network approaches for the cardinality estimation follow a global approach considering the whole database schema at once. These global models are prone to sparse data at training leading to misestimates for queries which were not represented in the sample space used for generating training queries. To overcome this issue, we introduce a novel local-oriented approach in this paper, therefore the local context is a specific sub-part of the schema. As we will show, this leads to better representation of data correlation and thus better estimation accuracy. Compared to global approaches, our novel approach achieves an improvement by two orders of magnitude in accuracy and by a factor of four in training time performance for local models.

Building the Dresden Web Table Corpus: A Classification Approach

Abstract: In recent years, researchers have recognized relational tables on the Web as an important source of information. To assist this research we developed the Dresden Web Tables Corpus (DWTC), a collection of about 125 million data tables extracted from the Common Crawl (CC) which contains 3.6 billion web pages and is 266TB in size. As the vast majority of HTML tables are used for layout purposes and only a small share contains genuine tables with different surface forms, accurate table detection is essential for building a large-scale Web table corpus. Furthermore, correctly recognizing the table structure (e.g. horizontal listings, matrices) is important in order to understand the role of each table cell, distinguishing between label and data cells. In this paper, we present an extensive table layout classification that enables us to identify the main layout categories of Web tables with very high precision. We therefore identify and develop a plethora of table features, different feature selection techniques and several classification algorithms. We evaluate the effectiveness of the selected features and compare the performance of various state-of-the-art classification algorithms. Finally, the winning approach is employed to classify millions of tables resulting in the Dresden Web Table Corpus (DWTC).

A Machine Learning Approach for Layout Inference in Spreadsheets

Abstract: Spreadsheet applications are one of the most used tools for content generation and presentation in industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and reuse the richness of data maintained in this format. The biggest obstacle is the lack of awareness about the structure of the data in spreadsheets, which otherwise could provide the means to automatically understand and extract knowledge from these files. In this paper, we propose a classification approach to discover the layout of tables in spreadsheets. Therefore, we focus on the cell level, considering a wide range of features not covered before by related work. We evaluated the performance of our classifiers on a large dataset covering three different corpora from various domains. Finally, our work includes a novel technique for detecting and repairing incorrectly classified cells in a post-processing step. The experimental results show that our approach delivers very high accuracy bringing us a crucial step closer towards automatic table extraction.

Table Recognition in Spreadsheets via a Graph Representation

Abstract: Spreadsheet software are very popular data management tools. Their ease of use and abundant functionalities equip novices and professionals alike with the means to generate, transform, analyze, and visualize data. As a result, spreadsheets are a great resource of factual and structured information. This accentuates the need to automatically understand and extract their contents. In this paper, we present a novel approach for recognizing tables in spreadsheets. Having inferred the layout role of the individual cells, we build layout regions. We encode the spatial interrelations between these regions using a graph representation. Based on this, we propose Remove and Conquer (RAC), an algorithm for table recognition that implements a list of carefully curated rules. An extensive experimental evaluation shows that our approach is viable. We achieve significant accuracy in a dataset of real spreadsheets from various domains.

Partition-based workload scheduling in living data warehouse environments

Abstract: The demand for so-called living or real-time data warehouses is increasing in many application areas such as manufacturing, event monitoring and telecommunications. In these fields users usually expect short response times for their queries and high freshness for the requested data. However, meeting these fundamental requirements is challenging due to the high loads and the continuous flow of write-only updates and read-only queries, which may be in conflict with each other. Therefore, we present the concept of Workload Balancing by Election (WINE), which allows users to express their individual demands on the Quality of Service and the Quality of Data respectively. WINE applies this information to balance and prioritize over both types of transactions -- queries and update -- according to the varying user needs. A simulation study shows that our proposed algorithm outperforms competitor baseline algorithms over the entire spectrum of workloads and user requirements.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Knowledge Graphs

Abstract: In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

A Survey of Extract–Transform–Load Technology

Abstract: The software processes that facilitate the original loading and the periodic refreshment of the data warehouse contents are commonly known as Extraction-Transformation-Loading (ETL) processes. The intention of this survey is to present the research work in the field of ETL technology in a structured way. To this end, we organize the coverage of the field as follows: (a) first, we cover the conceptual and logical modeling of ETL processes, along with some design methods, (b) we visit each stage of the E-T-L triplet, and examine problems that fall within each of these stages, (c) we discuss problems that pertain to the entirety of an ETL process, and, (d) we review some research prototypes of academic origin. [Article copies are available for purchase from InfoSci-on-Demand.com]

A Large Public Corpus of Web Tables containing Time and Context Metadata

Abstract: The Web contains vast amounts of HTML tables. Most of these tables are used for layout purposes, but a small subset of the tables is relational, meaning that they contain structured data describing a set of entities [2]. As these relational Web tables cover a very wide range of different topics, there is a growing body of research investigating the utility of Web table data for completing cross-domain knowledge bases [6], for extending arbitrary tables with additional attributes [7, 4], as well as for translating data values [5]. The existing research shows the potentials of Web tables. However, comparing the performance of the different systems is difficult as up till now each system is evaluated using a different corpus of Web tables and as most of the corpora are owned by large search engine companies and are thus not accessible to the public. In this poster, we present a large public corpus of Web tables which contains over 233 million tables and has been extracted from the July 2015 version of the CommonCrawl. By publishing the corpus as well as all tools that we used to extract it from the crawled data, we intend to provide a common ground for evaluating Web table systems. The main difference of the corpus compared to an earlier corpus that we extracted from the 2012 version of the CommonCrawl as well as the corpus extracted by Eberius et al. [3] from the 2014 version of the CommonCrawl is that the current corpus contains a richer set of metadata for each table. This metadata includes table-specific information such as table orientation, table caption, header row, and key column, but also context information such as the text before and after the table, the title of the HTML page, as well as timestamp information that was found before and after the table. The context information can be useful for recovering the semantics of a table [7]. The timestamp information is crucial for fusing time-depended data, such as alternative population numbers for a city [8].