arXiv subjects

Ingo Müller

Publications and source records attributed to Ingo Müller.

13 recordsLinked to original sources

arXiv2025

Addressing Visual Impairments with Model-Driven Engineering: A Systematic Literature Review

Software applications often pose barriers for users with accessibility needs, e.g., visual impairments. Model-driven engineering (MDE), with its systematic nature of code derivation, offers systematic methods to integrate accessibility concerns into software development while reducing manual effort. This paper presents a systematic literature review on how MDE addresses accessibility for vision impairments. From 447 initially identified papers, 30 primary studies met the inclusion criteria. About two-thirds reference the Web Content Accessibility Guidelines (WCAG), yet their project-specific adaptions and end-user validations hinder wider adoption in MDE. The analyzed studies model user interface structures, interaction and navigation, user capabilities, requirements, and context information. However, only few specify concrete modeling techniques on how to incorporate accessibility needs or demonstrate fully functional systems. Insufficient details on MDE methods, i.e., transformation rules or code templates, hinder the reuse, generalizability, and reproducibility. Furthermore, limited involvement of affected users and limited developer expertise in accessibility contribute to weak empirical validation. Overall, the findings indicate that current MDE research insufficiently supports vision-related accessibility. Our paper concludes with a research agenda outlining how support for vision impairments can be more effectively embedded in MDE processes.

cs.SE↗

arXiv2022

Resource Allocation in Serverless Query Processing

Data lakes hold a growing amount of cold data that is infrequently accessed, yet require interactive response times. Serverless functions are seen as a way to address this use case since they offer an appealing alternative to maintaining (and paying for) a fixed infrastructure. Recent research has analyzed the potential of serverless for data processing. In this paper, we expand on such work by looking into the question of serverless resource allocation to data processing tasks (number and size of the functions). We formulate a general model to roughly estimate completion time and financial cost, which we apply to augment an existing serverless data processing system with an advisory tool that automatically identifies configurations striking a good balance -- which we define as being close to the "knee" of their Pareto frontier. The model takes into account key aspects of serverless: start-up, computation, network transfers, and overhead as a function of the input sizes and intermediate result exchanges. Using (micro)benchmarks and parts of TPC-H, we show that this advisor is capable of pinpointing configurations desirable to the user. Moreover, we identify and discuss several aspects of data processing on serverless affecting efficiency. By using an automated tool to configure the resources, the barrier to using serverless for data processing is lowered and the narrow window where it is cost effective can be expanded by using a more optimal allocation instead of having to over-provision the design.

cs.DB↗

arXiv2022

Short-lived Datacenter

Serverless platforms have attracted attention due to their promise of elasticity, low cost, and fast deployment. Instead of using a fixed virtual machine (VM) infrastructure, which can incur considerable costs to operate and run, serverless platforms support short computations, triggered on demand, with cost proportional to fine-grain function execution time. However, serverless platforms offer a restricted execution environment. For example, functions have limited execution times, limited resources, and no support for networking between functions. In this paper, we explore what it takes to treat serverless platforms as short-lived, general purpose data-centers which can execute unmodified existing applications. As a first step in this quest, we have developed Boxer, a system providing an execution environment on top of existing functions-as-a-service platforms that allows users to seamlessly migrate conventional VM-based cloud services to serverless platforms. Boxer allows generic applications to benefit from the fine-grain elasticity of serverless platforms without having to modify applications to adopt a restrictive event-triggered programming model or orchestrate auxiliary systems for data communication. We implement Boxer on top of AWS Lambda and extend it to transparently provide standard network interfaces. We describe its implementation and demonstrate how it can be used to run off-the-shelf cloud applications with a degree of fine-grained elasticity not available on traditional VM-based platforms.

cs.DC↗

arXiv2021

Evaluating Query Languages and Systems for High-Energy Physics Data [Extended Version]

In the domain of high-energy physics (HEP), query languages in general and SQL in particular have found limited acceptance. This is surprising since HEP data analysis matches the SQL model well: the data is fully structured and queried using mostly standard operators. To gain insights on why this is the case, we perform a comprehensive analysis of six diverse, general-purpose data processing platforms using an HEP benchmark. The result of the evaluation is an interesting and rather complex picture of existing solutions: Their query languages vary greatly in how natural and concise HEP query patterns can be expressed. Furthermore, most of them are also between one and two orders of magnitude slower than the domain-specific system used by particle physicists today. These observations suggest that, while database systems and their query languages are in principle viable tools for HEP, significant work remains to make them relevant to HEP researchers.

cs.DB↗

arXiv2021

Modularis: Modular Relational Analytics over Heterogeneous Distributed Platforms

The enormous quantity of data produced every day together with advances in data analytics has led to a proliferation of data management and analysis systems. Typically, these systems are built around highly specialized monolithic operators optimized for the underlying hardware. While effective in the short term, such an approach makes the operators cumbersome to port and adapt, which is increasingly required due to the speed at which algorithms and hardware evolve. To address this limitation, we present Modularis, an execution layer for data analytics based on sub-operators, i.e.,composable building blocks resembling traditional database operators but at a finer granularity. To demonstrate the advantages of our approach, we use Modularis to build a distributed query processing system supporting relational queries running on an RDMA cluster, a serverless cloud platform, and a smart storage engine. Modularis requires minimal code changes to execute queries across these three diverse hardware platforms, showing that the sub-operator approach reduces the amount and complexity of the code. In fact, changes in the platform affect only sub-operators that depend on the underlying hardware. We show the end-to-end performance of Modularis by comparing it with a framework for SQL processing (Presto), a commercial cluster database (SingleStore), as well as Query-as-a-Service systems (Athena, BigQuery). Modularis outperforms all these systems, proving that the design and architectural advantages of a modular design can be achieved without degrading performance. We also compare Modularis with a hand-optimized implementation of a join for RDMA clusters. We show that Modularis has the advantage of being easily extensible to a wider range of join variants and group by queries, all of which are not supported in the hand-tuned join.

cs.DB↗

arXiv2020

Rumble: Data Independence for Large Messy Data Sets

This paper introduces Rumble, a query execution engine for large, heterogeneous, and nested collections of JSON objects built on top of Apache Spark. While data sets of this type are more and more wide-spread, most existing tools are built around a tabular data model, creating an impedance mismatch for both the engine and the query interface. In contrast, Rumble uses JSONiq, a standardized language specifically designed for querying JSON documents. The key challenge in the design and implementation of Rumble is mapping the recursive structure of JSON documents and JSONiq queries onto Spark's execution primitives based on tabular data frames. Our solution is to translate a JSONiq expression into a tree of iterators that dynamically switch between local and distributed execution modes depending on the nesting level. By overcoming the impedance mismatch in the engine, Rumble frees the user from solving the same problem for every single query, thus increasing their productivity considerably. As we show in extensive experiments, Rumble is able to scale to large and complex data sets in the terabyte range with a similar or better performance than other engines. The results also illustrate that Codd's concept of data independence makes as much sense for heterogeneous, nested data sets as it does on highly structured tables.

cs.DB↗

arXiv2020

The Collection Virtual Machine: An Abstraction for Multi-Frontend Multi-Backend Data Analysis

Getting the best performance from the ever-increasing number of hardware platforms has been a recurring challenge for data processing systems. In recent years, the advent of data science with its increasingly numerous and complex types of analytics has made this challenge even more difficult. In practice, system designers are overwhelmed by the number of combinations and typically implement only one analysis/platform combination, leading to repeated implementation effort -- and a plethora of semi-compatible tools for data scientists. In this paper, we propose the "Collection Virtual Machine" (or CVM) -- an extensible compiler framework designed to keep the specialization process of data analytics systems tractable. It can capture at the same time the essence of a large span of low-level, hardware-specific implementation techniques as well as high-level operations of different types of analyses. At its core lies a language for defining nested, collection-oriented intermediate representations (IRs). Frontends produce programs in their IR flavors defined in that language, which get optimized through a series of rewritings (possibly changing the IR flavor multiple times) until the program is finally expressed in an IR of platform-specific operators. While reducing the overall implementation effort, this also improves the interoperability of both analyses and hardware platforms. We have used CVM successfully to build specialized backends for platforms as diverse as multi-core CPUs, RDMA clusters, and serverless computing infrastructure in the cloud and expect similar results for many more frontends and hardware platforms in the near future.

cs.DB↗

arXiv2019

Lambada: Interactive Data Analytics on Cold Data using Serverless Cloud Infrastructure

The promise of ultimate elasticity and operational simplicity of serverless computing has recently lead to an explosion of research in this area. In the context of data analytics, the concept sounds appealing, but due to the limitations of current offerings, there is no consensus yet on whether or not this approach is technically and economically viable. In this paper, we identify interactive data analytics on cold data as a use case where serverless computing excels. We design and implement Lambada, a system following a purely serverless architecture, in order to illustrate when and how serverless computing should be employed for data analytics. We propose several system components that overcome the previously known limitations inherent in the serverless paradigm as well as additional ones we identify in this work. We can show that, thanks to careful design, a serverless query processing system can be at the same time one order of magnitude faster and two orders of magnitude cheaper compared to commercial Query-as-a-Service systems, the only alternative with similar operational simplicity.

cs.DB↗

arXiv2018

Pay One, Get Hundreds for Free: Reducing Cloud Costs through Shared Query Execution

Cloud-based data analysis is nowadays common practice because of the lower system management overhead as well as the pay-as-you-go pricing model. The pricing model, however, is not always suitable for query processing as heavy use results in high costs. For example, in query-as-a-service systems, where users are charged per processed byte, collections of queries accessing the same data frequently can become expensive. The problem is compounded by the limited options for the user to optimize query execution when using declarative interfaces such as SQL. In this paper, we show how, without modifying existing systems and without the involvement of the cloud provider, it is possible to significantly reduce the overhead, and hence the cost, of query-as-a-service systems. Our approach is based on query rewriting so that multiple concurrent queries are combined into a single query. Our experiments show the aggregated amount of work done by the shared execution is smaller than in a query-at-a-time approach. Since queries are charged per byte processed, the cost of executing a group of queries is often the same as executing a single one of them. As an example, we demonstrate how the shared execution of the TPC-H benchmark is up to 100x and 16x cheaper in Amazon Athena and Google BigQuery than using a query-at-a-time approach while achieving a higher throughput.

cs.DB↗

arXiv2018

Reproducible Floating-Point Aggregation in RDBMSs

Industry-grade database systems are expected to produce the same result if the same query is repeatedly run on the same input. However, the numerous sources of non-determinism in modern systems make reproducible results difficult to achieve. This is particularly true if floating-point numbers are involved, where the order of the operations affects the final result. As part of a larger effort to extend database engines with data representations more suitable for machine learning and scientific applications, in this paper we explore the problem of making relational GroupBy over floating-point formats bit-reproducible, i.e., ensuring any execution of the operator produces the same result up to every single bit. To that aim, we first propose a numeric data type that can be used as drop-in replacement for other number formats and is---unlike standard floating-point formats---associative. We use this data type to make state-of-the-art GroupBy operators reproducible, but this approach incurs a slowdown between 4x and 12x compared to the same operator using conventional database number formats. We thus explore how to modify existing GroupBy algorithms to make them bit-reproducible and efficient. By using vectorized summation on batches and carefully balancing batch size, cache footprint, and preprocessing costs, we are able to reduce the slowdown due to reproducibility to a factor between 1.9x and 2.4x of aggregation in isolation and to a mere 2.7% of end-to-end query performance even on aggregation-intensive queries in MonetDB. We thereby provide a solid basis for supporting more reproducible operations directly in relational engines. This document is an extended version of an article currently in print for the proceedings of ICDE'18 with the same title and by the same authors. The main additions are more implementation details and experiments.

cs.DB↗

arXiv2016

An Analysis of the Cloud Computing Security Problem

Cloud computing is a new computational paradigm that offers an innovative business model for organizations to adopt IT without upfront investment. Despite the potential gains achieved from the cloud computing, the model security is still questionable which impacts the cloud model adoption. The security problem becomes more complicated under the cloud model as new dimensions have entered into the problem scope related to the model architecture, multi-tenancy, elasticity, and layers dependency stack. In this paper we introduce a detailed analysis of the cloud security problem. We investigated the problem from the cloud architecture perspective, the cloud offered characteristics perspective, the cloud stakeholders' perspective, and the cloud service delivery models perspective. Based on this analysis we derive a detailed specification of the cloud security problem and key features that should be covered by any proposed security solution.

cs.SE↗

arXiv2015

Expansion of a spherical dust gas -- the cosmological conundrum

The universe is viewed as a dust gas filling a sphere and floating in infinite empty space. Einstein's gravitational equations are applied to this case together with appropriate boundary values. The equations are solved for initial conditions chosen so as to describe the observed Hubble diagram. We find that the solution is not unique so that more astronomical observations are needed. However, those solutions which were found do not exhibit an accelerated expansion of the universe, nor -- obviously then -- do they need the notion of a dark energy driving such an expansion. We present this study as an alternative to the prevailing Robertson-Walker cosmology.

gr-qc↗

arXiv2015

Communication Efficient Algorithms for Top-k Selection Problems

We present scalable parallel algorithms with sublinear per-processor communication volume and low latency for several fundamental problems related to finding the most relevant elements in a set, for various notions of relevance: We begin with the classical selection problem with unsorted input. We present generalizations with locally sorted inputs, dynamic content (bulk-parallel priority queues), and multiple criteria. Then we move on to finding frequent objects and top-k sum aggregation. Since it is unavoidable that the output of these algorithms might be unevenly distributed over the processors, we also explain how to redistribute this data with minimal communication.

cs.DS↗