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Many modern DBMS architectures require transferring data from storage to process it afterwards. Given the continuously increasing amounts of data, data transfers quickly become a scalability limiting factor. Near-Data Processing and smart/computational storage emerge as promising trends allowing for decoupled in-situ operation execution, data transfer reduction and better bandwidth utilization. However, not every operation is suitable for an in-situ execution and a careful placement and optimization is needed.
In this paper we present an NDP-aware cost model. It has been implemented in MySQL and evaluated with nKV. We make several observations underscoring the need for optimization.
In this paper we describe an interactive web-based tool for visual analysis of Formula 1 data. A calendar-like representation provides an overview of all races on a yearly basis, either in absolute or normalized time. After selecting a dedicated race more details about this race can be explored. Furthermore it is possible to compare up to three different races. Beside visualizing details on dedicated races it is also possible to analyse driver and team performance over time. A user study was applied to get feedback about the usage of the application and decide between different visualization options.
Painting galleries typically provide a wealth of data composed of several data types. Those multivariate data are too complex for laymen like museum visitors to first, get an overview about all paintings and to look for specific categories. Finally, the goal is to guide the visitor to a specific painting that he wishes to have a more closer look on. In this paper we describe an interactive visualization tool that first provides such an overview and lets people experiment with the more than 41,000 paintings collected in the web gallery of art. To generate such an interactive tool, our technique is composed of different steps like data handling, algorithmic transformations, visualizations, interactions, and the human user working with the tool with the goal to detect insights in the provided data. We illustrate the usefulness of the visualization tool by applying it to such characteristic data and show how one can get from an overview about all paintings to specific paintings.
In a time of digital transformation, the ability to quickly and efficiently adapt software systems to changed business requirements becomes more important than ever. Measuring the maintainability of software is therefore crucial for the long-term management of such products. With service-based systems (SBSs) being a very important form of enterprise software, we present a holistic overview of such metrics specifically designed for this type of system, since traditional metrics – e.g. object oriented ones – are not fully applicable in this case. The selected metric candidates from the literature review were mapped to 4 dominant design properties: size, complexity, coupling, and cohesion. Microservice-based systems (μSBSs) emerge as an agile and fine grained variant of SBSs. While the majority of identified metrics are also applicable to this specialization (with some limitations), the large number of services in combination with technological heterogeneity and decentralization of control significantly impacts automatic metric collection in such a system. Our research therefore suggests that specialized tool support is required to guarantee the practical applicability of the presented metrics to μSBSs.
The introduction of smart contracts has expanded the applicability of blockchains to many domains beyond finance and cryptocurrencies. Moreover, different blockchain technologies have evolved that target special requirements. As a result, in practice, often a combination of different blockchain systems is required to achieve an overall goal. However, due to the heterogeneity of blockchain protocols, the execution of distributed business transactions that span several blockchains leads to multiple interoperability and integration challenges. Therefore, in this article, we examine the domain of Cross-Chain Smart Contract Invocations (CCSCIs), which are distributed transactions that involve the invocation of smart contracts hosted on two or more blockchain systems. We conduct a systematic multi-vocal literature review to get an overview of the available CCSCI approaches. We select 20 formal literature studies and 13 high-quality gray literature studies, extract data from them, and analyze it to derive the CCSCI Classification Framework. With the help of the framework, we group the approaches into two categories and eight subcategories. The approaches differ in multiple characteristics, e.g., the mechanisms they follow, and the capabilities and transaction processing semantics they offer. Our analysis indicates that all approaches suffer from obstacles that complicate real-world adoption, such as the low support for handling heterogeneity and the need for trusted third parties.
Through increasing market dynamics, rapidly evolving technologies and shifting user expectations coupled with the adoption of lean and agile practices, companies are struggling with their ability to provide reliable product roadmaps by applying traditional approaches. Currently, most companies are seeking opportunities to improve their product roadmapping practices. As a first challenge they have to assess their current product roadmapping capabilities in order to better understand how to improve their practices and how to switch to a new approach. The aim of this article is to provide an initial maturity model for product roadmapping practices that is especially suited for assessing the roadmapping capabilities of companies operating in dynamic and uncertain market environments. Based on interviews with 15 experts from 13 various companies the current state of practice regarding product roadmapping was identified. Afterwards, the model development was conducted in the context of expert workshops with the Robert Bosch GmbH and researchers. The study results in the so-called DEEP 1.0 product roadmap maturity model which allows companies to conduct a self assessment of their product roadmapping practice.
Deep learning-based EEG detection of mental alertness states from drivers under ethical aspects
(2021)
One of the most critical factors for a successful road trip is a high degree of alertness while driving. Even a split second of inattention or sleepiness in a crucial moment, will make the difference between life and death. Several prestigious car manufacturers are currently pursuing the aim of automated drowsiness identification to resolve this problem. The path between neuro-scientific research in connection with artificial intelligence and the preservation of the dignity of human individual’s and its inviolability, is very narrow. The key contribution of this work is a system of data analysis for EEGs during a driving session, which draws on previous studies analyzing heart rate (ECG), brain waves (EEG), and eye function (EOG). The gathered data is hereby treated as sensitive as possible, taking ethical regulations into consideration. Obtaining evaluable signs of evolving exhaustion includes techniques that obtain sleeping stage frequencies, problematic are hereby the correlated interference’s in the signal. This research focuses on a processing chain for EEG band splitting that involves band-pass filtering, principal component analysis (PCA), independent component analysis (ICA) with automatic artefact severance, and fast fourier transformation (FFT). The classification is based on a step-by-step adaptive deep learning analysis that detects theta rhythms as a drowsiness predictor in the pre-processed data. It was possible to obtain an offline detection rate of 89% and an online detection rate of 73%. The method is linked to the simulated driving scenario for which it was developed. This leaves space for more optimization on laboratory methods and data collection during wakefulness-dependent operations.
Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. Every project is unique and, thus, many context factors have to be considered. Recent research took some initial steps towards statistically constructing hybrid development methods, yet, paid little attention to the peculiarities of context factors influencing method and practice selection. In this paper, we utilize exploratory factor analysis and logistic regression analysis to learn such context factors and to identify methods that are correlated with these factors. Our analysis is based on 829 data points from the HELENA dataset. We provide five base clusters of methods consisting of up to 10 methods that lay the foundation for devising hybrid development methods. The analysis of the five clusters using trained models reveals only a few context factors, e.g., project/product size and target application domain, that seem to significantly influence the selection of methods. An extended descriptive analysis of these practices in the context of the identified method clusters also suggests a consolidation of the relevant practice sets used in specific project contexts.
Blockchains yield to new workloads in database management systems and K/V-stores. Distributed Ledger Technology (DLT) is a technique for managing transactions in ’trustless’ distributed systems. Yet, clients of nodes in blockchain networks are backed by ’trustworthy’ K/V-Stores, like LevelDB or RocksDB in Ethereum, which are based on Log-Structured Merge Trees (LSM Trees). However, LSM-Trees do not fully match the properties of blockchains and enterprise workloads.
In this paper, we claim that Partitioned B-Trees (PBT) fit the properties of this DLT: uniformly distributed hash keys, immutability, consensus, invalid blocks, unspent and off-chain transactions, reorganization and data state / version ordering in a distributed log-structure. PBT can locate records of newly inserted key-value pairs, as well as data of unspent transactions, in separate partitions in main memory. Once several blocks acquire consensus, PBTs evict a whole partition, which becomes immutable, to secondary storage. This behavior minimizes write amplification and enables a beneficial sequential write pattern on modern hardware. Furthermore, DLT implicate some type of log-based versioning. PBTs can serve as MV-store for data storage of logical blocks and indexing in multi-version concurrency control (MVCC) transaction processing.
First International Workshop on Hybrid dEveLopmENt Approaches in Software Systems Development
(2017)
A software process is the game plan to organize project teams and run projects. Yet, it still is a challenge to select the appropriate development approach for the respective context. A multitude of development approaches compete for the users’ favor, but there is no silver bullet serving all possible setups. Moreover, recent research as well as experience from practice shows companies utilizing different development approaches to assemble the bestfitting approach for the respective company: a more traditional process provides the basic framework to serve the organization, while project teams embody this framework with more agile (and/or lean) practices to keep their flexibility. The first HELENA workshop aims to bring together the community to discuss recent findings and to steer future work.