As data volumes continue to grow, clustering and outlier detection algorithms are becoming increasingly time-consuming. Classical index structures for neighbor search are no longer sustainable due to the "curse of dimensionality". Instead, approximated index structures offer a good opportunity to significantly accelerate the neighbor search for clustering and outlier detection and to have the lowest

Complex scientific experiments from various domains are typically modeled as workflows and executed on large-scale machines using a Parallel Workflow Management System (WMS). Since such executions usually last for hours or days, some WMSs provide user steering support, i.e., they allow users to run data analyses and, depending on the results, adapt the workflows at runtime. A challenge in the parallel

In many real-world applications such as business planning and sensor data monitoring, one important, yet challenging, the task is to rank objects(e.g., products, documents, or spatial objects) based on their ranking scores and efficiently return those objects with the highest scores. In practice, due to the unreliability of data sources, many real-world objects often contain noises and are thus imprecise

In the data center, unexpected downtime caused by memory failures can lead to a decline in the stability of the server and even the entire information technology infrastructure, which harms the business. Therefore, whether the memory failure can be accurately predicted in advance has become one of the most important issues to be studied in the data center. However, for the memory failure prediction

Graph transaction processing raises many unique challenges such as random data access due to the irregularity of graph structures, low throughput and high abort rate due to the relatively large read/write sets in graph transactions. To address these challenges, we present G-Tran -- an RDMA-enabled distributed in-memory graph database with serializable and snapshot isolation support. First, we propose

Due to the large volume of data and information generated by a multitude of social data sources, it is a huge challenge to manage and extract useful knowledge, especially given the different forms of data, streaming data and uncertainty and ambiguity of data. Hence, there are still challenges in this area of BD analytics research to capture, store, process, visualise, query, and manipulate datasets

In the Open Data Portal Germany (OPAL) project, a pipeline of the following data refinement steps has been developed: requirements analysis, data acquisition, analysis, conversion, integration and selection. 800,000 datasets in DCAT format have been produced.

Focusing on comprehensive networking, big data, and artificial intelligence, the Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations. Various sensors and field devices play a central role, as they generate a vast amount of real-time data that can provide insights into manufacturing. The synergy of complex event processing (CEP) and machine learning (ML)

This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detecting and observing the spread of misinformation about this disease within texts. CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts. DS pipeline data preparation tasks extract features from multilingual textual

Recently, the database management system (DBMS) community has witnessed the power of machine learning (ML) solutions for DBMS tasks. Despite their promising performance, these existing solutions can hardly be considered satisfactory. First, these ML-based methods in DBMS are not effective enough because they are optimized on each specific task, and cannot explore or understand the intrinsic connections

Compiling commonsense knowledge is traditionally an AI topic approached by manual labor. Recent advances in web data processing have enabled automated approaches. In this demonstration we will showcase three systems for automated commonsense knowledge base construction, highlighting each time one aspect of specific interest to the data management community. (i) We use Quasimodo to illustrate knowledge

This paper considers enumerating answers to similarity-join queries under dynamic updates: Given two sets of $n$ points $A,B$ in $\mathbb{R}^d$, a metric $\phi(\cdot)$, and a distance threshold $r > 0$, report all pairs of points $(a, b) \in A \times B$ with $\phi(a,b) \le r$. Our goal is to store $A,B$ into a dynamic data structure that, whenever asked, can enumerate all result pairs with worst-case

There is a growing trend regarding perceiving personal data as a commodity. Existing studies have built frameworks and theories about how to determine an arbitrage-free price of a given query according to the privacy loss quantified by differential privacy. However, those previous works have assumed that data buyers can purchase query answers with the arbitrary privacy loss of data owners, which may

Process analytics is the field focusing on predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling next activity, remaining time, and outcome prediction. At the model level, there is a notable void. It is the ambition of this paper to fill this gap. To this end, we develop a technique to forecast the

The recently released persistent memory (PM) has been gaining popularity. PM offers high performance (still lower than DRAM), persistence, and is cheaper than DRAM. This opens up new possibilities for index design. Existing indexes on PM typically evolve traditional B+tree indexes. However, this is not the best choice. Recently proposed learned indexes use machine learning (ML) for data distribution-aware

In this paper, we present our vision of so called zero-shot learning for databases which is a new learning approach for database components. Zero-shot learning for databases is inspired by recent advances in transfer learning of models such as GPT-3 and can support a new database out-of-the box without the need to train a new model. As a first concrete contribution in this paper, we show the feasibility

Conversational interfaces to Business Intelligence (BI) applications enable data analysis using a natural language dialog in small incremental steps. To truly unleash the power of conversational BI to democratize access to data, a system needs to provide effective and continuous support for data analysis. In this paper, we propose BI-REC, a conversational recommendation system for BI applications to

This paper introduces LACLICHEV (Latin American Climate Change Evolution platform ), a data collections exploration environment for exploring historical newspapers searching for articles reporting meteorological events. LACLICHEV is based on data collections' exploration techniques combined with information retrieval, data analytics, and geographic querying and visualization. This environment provides

Exploratory data science largely happens in computational notebooks with dataframe API, such as pandas, that support flexible means to transform, clean, and analyze data. Yet, visually exploring data in dataframes remains tedious, requiring substantial programming effort for visualization and mental effort to determine what analysis to perform next. We propose Lux, an always-on framework for accelerating

Constraint satisfaction problems (CSP's) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities

Location data are widely used in mobile apps, ranging from location-based recommendations, to social media and navigation. A specific type of interaction is that of location-based alerts, where mobile users subscribe to a service provider (SP) in order to be notified when a certain event occurs nearby. Consider, for instance, the ongoing COVID-19 pandemic, where contact tracing has been singled out

Interpreted execution of queries, as in the vectorized model, suffers from interpretation overheads. By compiling queries this interpretation overhead is eliminated at the cost of a compilation phase that delays execution, sacrificing latency for throughput. For short-lived queries, minimizing latency is important, while for long-running queries throughput outweighs latency. Because neither a purely

JSON Schema is an evolving standard for the description of families of JSON documents. JSON Schema is a logical language, based on a set of assertions that describe features of the JSON value under analysis and on logical or structural combinators for these assertions. As for any logical language, problems like satisfaction, not-elimination, schema satisfiability, schema inclusion and equivalence,

In this paper, we address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables. Embeddings help to capture semantics encoded in the database and can be used in a variety of settings like auto-completion of tables, fully-neural query processing of relational joins queries, seamlessly handling missing values, and more. Current work is

We show how to achieve fast autocompletion for SPARQL queries on very large knowledge bases. At any position in the body of a SPARQL query, the autocompletion suggests matching subjects, predicates, or objects. The suggestions are context-sensitive in the sense that they lead to a non-empty result and are ranked by their relevance to the part of the query already typed. The suggestions can be narrowed

Modern trends in data collection are bringing current mainstream techniques for database query processing to their limits. Consequently, various novel approaches for efficient query processing are being actively studied. One such approach is based on hypertree decompositions (HDs), which have been shown to carry great potential to process complex queries more efficiently and with stronger theoretical

The problem of natural language processing over structured data has become a growing research field, both within the relational database and the Semantic Web community, with significant efforts involved in question answering over knowledge graphs (KGQA). However, many of these approaches are either specifically targeted at open-domain question answering using DBpedia, or require large training datasets

Application developers, in our experience, tend to hesitate when dealing with linked data technologies. To reduce their initial hurdle and enable rapid prototyping, we propose in this paper a framework for building linked data applications. Our approach especially considers the participation of web developers and non-technical users without much prior knowledge about linked data concepts. Web developers

The RDF Mapping Language (RML) enables, among other formats, the mapping of tabular data as Comma-Separated Values (CSV) files to RDF graphs. Unfortunately, the widely used spreadsheet format is currently neglected by its specification and well-known implementations. Therefore, we extended one of the tools which is RML Mapper to support Microsoft Excel spreadsheet files and demonstrate its capabilities

Confidentiality hinders the publication of authentic, labeled datasets of personal and enterprise data, although they could be useful for evaluating knowledge graph construction approaches in industrial scenarios. Therefore, our plan is to synthetically generate such data in a way that it appears as authentic as possible. Based on our assumption that knowledge workers have certain habits when they

Travel time or speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or aggregation and represent different trade-offs between generalizability and accuracy. Function-fitting approaches learn functions that map feature vectors of, e.g., routes, to travel time or speed estimates, which enables generalization to unseen

Database systems analyze queries to determine upfront which data is needed for answering them and use indexes and other physical design techniques to speed-up access to that data. However, for important classes of queries, e.g., HAVING and top-k queries, it is impossible to determine up-front what data is relevant. To overcome this limitation, we develop provenance-based data skipping (PBDS), a novel

This dissertation proposes the study of multimodal learning in the context of musical signals. Throughout, we focus on the interaction between audio signals and text information. Among the many text sources related to music that can be used (e.g. reviews, metadata, or social network feedback), we concentrate on lyrics. The singing voice directly connects the audio signal and the text information in

The performance of applications, such as personal assistants, search engines, and question-answering systems, rely on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs). To ensure their quality one important task is Knowledge Validation, which measures the degree to which statements or triples of a Knowledge Graph (KG) are correct. KGs inevitably contains incorrect and incomplete statements

Query languages in general and SQL in particular are arguably one of the most successful programming interfaces. Yet, in the domain of high-energy physics (HEP), they have found limited acceptance. This is surprising since data analysis in HEP matches the SQL model well: it is fully structured data queried using combinations of selections, projections, joins, and reductions. To gain insights on why

During the development of NoSQL-backed software, the data model evolves naturally alongside the application code. Especially in agile development, new application releases are deployed frequently causing schema changes. Eventually, decisions have to be made regarding the migration of versioned legacy data which is persisted in the cloud-hosted production database. We solve this schema evolution problem

In this paper, we propose a fresh perspective on argumentation semantics, to view them as a relational database. It offers encapsulation of the underlying argumentation graph, and allows us to understand argumentation semantics under a single, relational perspective, leading to the concept of relational argumentation semantics. This is a direction to understand argumentation semantics through a common

Indexing intervals is a fundamental problem, finding a wide range of applications. Recent work on managing large collections of intervals in main memory focused on overlap joins and temporal aggregation problems. In this paper, we propose novel and efficient in-memory indexing techniques for intervals, with a focus on interval range queries, which are a basic component of many search and analysis tasks

Given two relations containing multiple measurements - possibly with uncertainties - our objective is to find which sets of attributes from the first have a corresponding set on the second, using exclusively a sample of the data. This approach could be used even when the associated metadata is damaged, missing or incomplete, or when the volume is too big for exact methods. This problem is similar to

Computing shortest paths is a fundamental operation in processing graph data. In many real-world applications, discovering shortest paths between two vertices empowers us to make full use of the underlying structure to understand how vertices are related in a graph, e.g. the strength of social ties between individuals in a social network. In this paper, we study the shortest-path-graph problem that

Record linkage is aimed at the accurate and efficient identification of records that represent the same entity within or across disparate databases. It is a fundamental task in data integration and increasingly required for accurate decision making in application domains ranging from health analytics to national security. Traditional record linkage techniques calculate string similarities between quasi-identifying

Geolocated time series, i.e., time series associated with certain locations, abound in many modern applications. In this paper, we consider hybrid queries for retrieving geolocated time series based on filters that combine spatial distance and time series similarity. For the latter, unlike existing work, we allow filtering based on local similarity, which is computed based on subsequences rather than

Given a social network, where each user is associated with a selection cost, the problem of \textsc{Budgeted Influence Maximization} (\emph{BIM Problem} in short) asks to choose a subset of them (known as seed users) within an allocated budget whose initial activation leads to the maximum number of influenced nodes. Existing Studies on this problem do not consider the tag-specific influence probability

Inverted indexes continue to be a mainstay of text search engines, allowing efficient querying of large document collections. While there are a number of possible organizations, document-ordered indexes are the most common, since they are amenable to various query types, support index updates, and allow for efficient dynamic pruning operations. One disadvantage with document-ordered indexes is that

Services and products are often offered via the execution of processes that vary according to the context, requirements, or customisation needs. The analysis of such process variants can highlight differences in the service outcome or quality, leading to process adjustments and improvement. Research in the area of process mining has provided several methods for process variants analysis. However, very

We address the joint optimization of multiple stream joins in a scale-out architecture by tailoring prior work on multi-way stream joins to predicate-driven data partitioning schemes. We present an integer linear programming (ILP) formulation for selecting the partitioning and tuple routing with minimal probe load and describe how routing and operator placement can be rewired dynamically at changing

Recent advances in reprogrammable hardware (e.g., FPGAs) and memory technology (e.g., DDR4, HBM) promise to solve performance problems inherent to graph processing like irregular memory access patterns on traditional hardware (e.g., CPU). While several of these graph accelerators were proposed in recent years, it remains difficult to assess their performance and compare them on common graph workloads

Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute pattern (CAP) mining discovers correlations among multiple attributes from the sets of sensors spatially close to each other and temporally correlated

The number of databases as well as their size and complexity is increasing. This creates a barrier to use especially for non-experts, who have to come to grips with the nature of the data, the way it has been represented in the database, and the specific query languages or user interfaces by which data are accessed. These difficulties worsen in research settings, where it is common to work with many

Given a social network of users with selection cost, the \textsc{Budgeted Influence Maximization Problem} (\emph{BIM Problem} in short) asks for selecting a subset of the nodes (known as \emph{seed nodes}) within an allocated budget for initial activation to maximize the influence in the network. In this paper, we study this problem under the \emph{co\mbox{-}operative game theoretic} framework. We

MCAS (Memory Centric Active Storage) is a persistent memory tier for high-performance durable data storage. It is designed from the ground-up to provide a key-value capability with low-latency guarantees and data durability through memory persistence and replication. To reduce data movement and make further gains in performance, we provide support for user-defined "push-down" operations (known as Active

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. In

Detection and localization of actions in videos is an important problem in practice. A traffic analyst might be interested in studying the patterns in which vehicles move at a given intersection. State-of-the-art video analytics systems are unable to efficiently and effectively answer such action queries. The reasons are threefold. First, action detection and localization tasks require computationally

User authorization queries in the context of role-based access control have attracted considerable interest in the last 15 years. Such queries are used to determine whether it is possible to allocate a set of roles to a user that enables the user to complete a task, in the sense that all the permissions required to complete the task are assigned to the roles in that set. Answering such a query, in

Index plays an essential role in modern database engines to accelerate the query processing. The new paradigm of ``learned index'' has significantly changed the way of designing index structures in DBMS. The key insight is that indexes could be regarded as learned models that predict the position of a lookup key in the dataset. While such studies show promising results in both lookup time and index

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the

Nearest Neighbor Search (NNS) over generalized weighted distance is fundamental to a wide range of applications. The problem of NNS over the generalized weighted Square Euclidean distance has been studied in previous work. However, numerous studies have shown that the Manhattan distance could be more practical than the Euclidean distance for high-dimensional NNS. To the best of our knowledge, no prior

This paper investigates regex CQs with string equalities (SERCQs), a subclass of core spanners. As shown by Freydenberger, Kimelfeld, and Peterfreund (PODS 2018), these queries are intractable, even if restricted to acyclic queries. This previous result defines acyclicity by treating regex formulas as atoms. In contrast to this, we propose an alternative definition by converting SERCQs into FC-CQs

With the growing size of data sets, feature selection becomes increasingly important. Taking interactions of original features into consideration will lead to extremely high dimension, especially when the features are categorical and one-hot encoding is applied. This makes it more worthwhile mining useful features as well as their interactions. Association rule mining aims to extract interesting correlations

Complex data pipelines are increasingly common in diverse applications such as BI reporting and ML modeling. These pipelines often recur regularly (e.g., daily or weekly), as BI reports need to be refreshed, and ML models need to be retrained. However, it is widely reported that in complex production pipelines, upstream data feeds can change in unexpected ways, causing downstream applications to break