Evolution Management in Multi-Model Databases

Multi-model databases allow us to combine the advantages of various data models by storing different types of data in different models. However, this technology is still relatively immature, lacks standardization, and there are not any tools that would allow us to model multi-model data or manage their evolution. This thesis (i) provides an in- troduction to MM-cat, i.e., the framework for modeling multi-model data, (ii) describes the implementation of the framework, (iii) designs a workflow and a set of schema modi- fication operations to facilitate evolution management and (iv) performs experiments to prove their reliability and scalability. 1Multi-modelové databáze umožňují kombinovat výhody více datových modelů tím, že růzé typy dat ukládájí do různých modelů. Nicméně, tato technologie ještě není příliš vyspělá a postrádá standardizaci. Navíc neexistují nástroje, které by uměly modelovat multi-modelová data či spravovat jejich vývoj. Tato práce (i) slouží jako úvod do MM-cat, což je framework pro modelování multi-modelových dat, (ii) popisuje implementaci to- hoto frameworku, (iii) navrhuje workflow a zároveň i soubor schéma upravujících operací pro správu změn multi-modelových databází a (iv) provádí experimenty s cílem dokázat spolehlivost a škálovatelnost tohoto přístupu. 1Department of Software EngineeringKatedra softwarového inženýrstvíMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

Multi-model Database Management Systems - A Look Forward

Peer reviewe

A Formal Category Theoretical Framework for Multi-model Data Transformations

Data integration and migration processes in polystores and multi-model database management systems highly benefit from data and schema transformations. Rigorous modeling of transformations is a complex problem. The data and schema transformation field is scattered with multiple different transformation frameworks, tools, and mappings. These are usually domain-specific and lack solid theoretical foundations. Our first goal is to define category theoretical foundations for relational, graph, and hierarchical data models and instances. Each data instance is represented as a category theoretical mapping called a functor. We formalize data and schema transformations as Kan lifts utilizing the functorial representation for the instances. A Kan lift is a category theoretical construction consisting of two mappings satisfying the certain universal property. In this work, the two mappings correspond to schema transformation and data transformation.Peer reviewe

Strategy Formation Framework for Technology Adoption in Supply Chain Management

Firms have been seeking the strategies for technologies adoption to improve the internal performance and to streamline the processes with both partners. Emerging IT and manufacturing technology are the main driving forces. To form competitive strategies, the companies need to know the current situation, the projected position, and the technology to adopt to achieve the goal. A framework of strategy position map is presented to locate, set and move company’s strategy position based on IT adoption and corporate focus. Case studies on the firms from different parts in the supply chain for various industrial sectors demonstrate the applications of the proposed framework. The paper provides a discussion on the issues of observations, guidelines, components selection, and implications. The paper concludes strategy position map can help companies to form the strategy for supply chain management, and highlights the challenging coordination for intra-firm and inter-firm strategy formation

UniBench: A Benchmark for Multi-Model Database Management Systems

Unlike traditional database management systems which are organized around a single data model, a multi-model database (MMDB) utilizes a single, integrated back-end to support multiple data models, such as document, graph, relational, and key-value. As more and more platforms are proposed to deal with multi-model data, it becomes crucial to establish a benchmark for evaluating the performance and usability of MMDBs. Previous benchmarks, however, are inadequate for such scenario because they lack a comprehensive consideration for multiple models of data. In this paper, we present a benchmark, called UniBench, with the goal of facilitating a holistic and rigorous evaluation of MMDBs. UniBench consists of a mixed data model, a synthetic multi-model data generator, and a set of core workloads. Specifically, the data model simulates an emerging application: Social Commerce, a Web-based application combining E-commerce and social media. The data generator provides diverse data format including JSON, XML, key-value, tabular, and graph. The workloads are comprised of a set of multi-model queries and transactions, aiming to cover essential aspects of multi-model data management. We implemented all workloads on ArangoDB and OrientDB to illustrate the feasibility of our proposed benchmarking system and show the learned lessons through the evaluation of these two multi-model databases. The source code and data of this benchmark can be downloaded at http://udbms.cs.helsinki.fi/bench/.Peer reviewe

Requirements for a global data infrastructure in support of CMIP6

The World Climate Research Programme (WCRP)’s Working Group on Climate Modelling (WGCM) Infrastructure Panel (WIP) was formed in 2014 in response to the explosive growth in size and complexity of Coupled Model Intercomparison Projects (CMIPs) between CMIP3 (2005–2006) and CMIP5 (2011–2012). This article presents the WIP recommendations for the global data infrastruc- ture needed to support CMIP design, future growth, and evolution. Developed in close coordination with those who build and run the existing infrastructure (the Earth System Grid Federation; ESGF), the recommendations are based on several principles beginning with the need to separate requirements, implementation, and operations. Other im- portant principles include the consideration of the diversity of community needs around data – a data ecosystem – the importance of provenance, the need for automation, and the obligation to measure costs and benefits. This paper concentrates on requirements, recognizing the diversity of communities involved (modelers, analysts, soft- ware developers, and downstream users). Such requirements include the need for scientific reproducibility and account- ability alongside the need to record and track data usage. One key element is to generate a dataset-centric rather than system-centric focus, with an aim to making the infrastruc- ture less prone to systemic failure. With these overarching principles and requirements, the WIP has produced a set of position papers, which are summa- rized in the latter pages of this document. They provide spec- ifications for managing and delivering model output, includ- ing strategies for replication and versioning, licensing, data quality assurance, citation, long-term archiving, and dataset tracking. They also describe a new and more formal approach for specifying what data, and associated metadata, should be saved, which enables future data volumes to be estimated, particularly for well-defined projects such as CMIP6. The paper concludes with a future facing consideration of the global data infrastructure evolution that follows from the blurring of boundaries between climate and weather, and the changing nature of published scientific results in the digital age

Optimized Ensemble Approach for Multi-model Event Detection in Big data

Event detection acts an important role among modern society and it is a popular computer process that permits to detect the events automatically. Big data is more useful for the event detection due to large size of data. Multimodal event detection is utilized for the detection of events using heterogeneous types of data. This work aims to perform for classification of diverse events using Optimized Ensemble learning approach. The Multi-modal event data including text, image and audio are sent to the user devices from cloud or server where three models are generated for processing audio, text and image. At first, the text, image and audio data is processed separately. The process of creating a text model includes pre-processing using Imputation of missing values and data normalization. Then the textual feature extraction using integrated N-gram approach. The Generation of text model using Convolutional two directional LSTM (2DCon_LSTM). The steps involved in image model generation are pre-processing using Min-Max Gaussian filtering (MMGF). Image feature extraction using VGG-16 network model and generation of image model using Tweaked auto encoder (TAE) model. The steps involved in audio model generation are pre-processing using Discrete wavelet transform (DWT). Then the audio feature extraction using Hilbert Huang transform (HHT) and Generation of audio model using Attention based convolutional capsule network (Attn_CCNet). The features obtained by the generated models of text, image and audio are fused together by feature ensemble approach. From the fused feature vector, the optimal features are trained through improved battle royal optimization (IBRO) algorithm. A deep learning model called Convolutional duo Gated recurrent unit with auto encoder (C-Duo GRU_AE) is used as a classifier. Finally, different types of events are classified where the global model are then sent to the user devices with high security and offers better decision making process. The proposed methodology achieves better performances are Accuracy (99.93%), F1-score (99.91%), precision (99.93%), Recall (99.93%), processing time (17seconds) and training time (0.05seconds). Performance analysis exceeds several comparable methodologies in precision, recall, accuracy, F1 score, training time, and processing time. This designates that the proposed methodology achieves improved performance than the compared schemes. In addition, the proposed scheme detects the multi-modal events accurately