Run blazingly fast queries on complex graph datasets with the power of the Neo4j graph database About This Book Get acquainted with graph database systems and apply them in real-world use cases Use Cypher query language, APOC and other Neo4j extensions to derive meaningful analysis from complex data sets. A practical guide filled with ready to use examples on querying, graph processing and visualizing information to build smarter spatial applications. Who This Book Is For This book is for developers who want an alternative way to store and process data within their applications. No previous graph database experience is required; however, some basic database knowledge will help you understand the concepts more easily. What You Will Learn Understand the science of graph theory, databases and its advantages over traditional databases. Install Neo4j, model data and learn the most common practices of traversing data Learn the Cypher query language and tailor-made procedures to analyze and derive meaningful representations of data Improve graph techniques with the help of precise procedures in the APOC library Use Neo4j advanced extensions and plugins for performance optimization. Understand how Neo4j's new security features and clustering architecture are used for large scale deployments. In Detail Neo4j is a graph database that allows traversing huge amounts of data with ease. This book aims at quickly getting you started with the popular graph database Neo4j. Starting with a brief introduction to graph theory, this book will show you the advantages of using graph databases along with data modeling techniques for graph databases. You'll gain practical hands-on experience with commonly used and lesser known features for updating graph store with Neo4j's Cypher query language. Furthermore, you'll also learn to create awesome procedures using APOC and extend Neo4j's functionality, enabling integration, algorithmic analysis, and other advanced spatial operation capabilities on data. Through the course of the book you will come across implementation examples on the latest updates in Neo4j, such as in-graph indexes, scaling, performance improvements, visualization, data refactoring techniques, security enhancements, and much more. By the end of the book, you'll have gained the skills to design and implement modern spatial applications, from graphing data to unraveling business capabilities with the help of real-world use cases. Style and approach A step-by-step approach of adopting Neo4j, the world's leading graph database. This book includes a lot of background information, helps you grasp the fundamental concepts behind this radical new way of dealing with connected data, and will give you lots of examples of use cases and environments where a graph database would be a great fit

This book is for developers who want an alternative way to store and process data within their applications. No previous graph database experience is required; however, some basic database knowledge will help you understand the concepts more easily.

Build a strong and efficient IoT infrastructure at industrial and enterprise level by mastering Industrial IoT network Key FeaturesGain hands-on experience working with industrial architectureExplore the potential of cloud-based Industrial IoT platforms, analytics, and protocolsImprove business models and transform your workforce with Industry 4.0Book Description We live in an era where advanced automation is used to achieve accurate results. To set up an automation environment, you need to first configure a network that can be accessed anywhere and by any device. This book is a practical guide that helps you discover the technologies and use cases for Industrial Internet of Things (IIOT). Hands-On Industrial Internet of Things takes you through the implementation of industrial processes and specialized control devices and protocols. You’ll study the process of identifying and connecting to different industrial data sources gathered from different sensors. Furthermore, you’ll be able to connect these sensors to cloud network, such as AWS IoT, Azure IoT, Google IoT, and OEM IoT platforms, and extract data from the cloud to your devices. As you progress through the chapters, you’ll gain hands-on experience in using open source Node-Red, Kafka, Cassandra, and Python. You will also learn how to develop streaming and batch-based Machine Learning algorithms. By the end of this book, you will have mastered the features of Industry 4.0 and be able to build stronger, faster, and more reliable IoT infrastructure in your Industry. What you will learnExplore industrial processes, devices, and protocolsDesign and implement the I-IoT network flowGather and transfer industrial data in a secure wayGet to grips with popular cloud-based platformsUnderstand diagnostic analytics to answer critical workforce questionsDiscover the Edge device and understand Edge and Fog computingImplement equipment and process management to achieve business-specific goalsWho this book is for If you’re an IoT architect, developer, or stakeholder working with architectural aspects of Industrial Internet of Things, this book is for you.

This book presents a comprehensive overview of fundamental issues and recent advances in graph data management. Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains. The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions. The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs.

A hands-on guide to leveraging NoSQL databases NoSQL databases are an efficient and powerful tool for storing and manipulating vast quantities of data. Most NoSQL databases scale well as data grows. In addition, they are often malleable and flexible enough to accommodate semi-structured and sparse data sets. This comprehensive hands-on guide presents fundamental concepts and practical solutions for getting you ready to use NoSQL databases. Expert author Shashank Tiwari begins with a helpful introduction on the subject of NoSQL, explains its characteristics and typical uses, and looks at where it fits in the application stack. Unique insights help you choose which NoSQL solutions are best for solving your specific data storage needs. Professional NoSQL: Demystifies the concepts that relate to NoSQL databases, including column-family oriented stores, key/value databases, and document databases. Delves into installing and configuring a number of NoSQL products and the Hadoop family of products. Explains ways of storing, accessing, and querying data in NoSQL databases through examples that use MongoDB, HBase, Cassandra, Redis, CouchDB, Google App Engine Datastore and more. Looks at architecture and internals. Provides guidelines for optimal usage, performance tuning, and scalable configurations. Presents a number of tools and utilities relating to NoSQL, distributed platforms, and scalable processing, including Hive, Pig, RRDtool, Nagios, and more.

The field of data science, big data, machine learning, and artificial intelligence is exciting and complex at the same time. Data science is also rapidly growing with new tools, technologies, algorithms, datasets, and use cases. For a beginner in this field, the learning curve can be fairly daunting. This is where this book helps. The data science solutions book provides a repeatable, robust, and reliable framework to apply the right-fit workflows, strategies, tools, APIs, and domain for your data science projects. This book takes a solutions focused approach to data science. Each chapter meets an end-to-end objective of solving for data science workflow or technology requirements. At the end of each chapter you either complete a data science tools pipeline or write a fully functional coding project meeting your data science workflow requirements. SEVEN STAGES OF DATA SCIENCE SOLUTIONS WORKFLOW Every chapter in this book will go through one or more of these seven stages of data science solutions workflow. STAGE 1: Question. Problem. Solution. Before starting a data science project we must ask relevant questions specific to our project domain and datasets. We may answer or solve these during the course of our project. Think of these questions-solutions as the key requirements for our data science project. Here are some templates that can be used to frame questions for our data science projects. Can we classify an entity based on given features if our data science model is trained on certain number of samples with similar features related to specific classes?Do the samples, in a given dataset, cluster in specific classes based on similar or correlated features?Can our machine learning model recognise and classify new inputs based on prior training on a sample of similar inputs?STAGE 2: Acquire. Search. Create. Catalog.This stage involves data acquisition strategies including searching for datasets on popular data sources or internally within your organisation. We may also create a dataset based on external or internal data sources. The acquire stage may feedback to the question stage, refining our problem and solution definition based on the constraints and characteristics of the acquired datasets. STAGE 3: Wrangle. Prepare. Cleanse.The data wrangle phase prepares and cleanses our datasets for our project goals. This workflow stage starts by importing a dataset, exploring the dataset for its features and available samples, preparing the dataset using appropriate data types and data structures, and optionally cleansing the data set for creating model training and solution testing samples. The wrangle stage may circle back to the acquire stage to identify complementary datasets to combine and complete the existing dataset. STAGE 4: Analyse. Patterns. Explore.The analyse phase explores the given datasets to determine patterns, correlations, classification, and nature of the dataset. This helps determine choice of model algorithms and strategies that may work best on the dataset. The analyse stage may also visualize the dataset to determine such patterns. STAGE 5: Model. Predict. Solve.The model stage uses prediction and solution algorithms to train on a given dataset and apply this training to solve for a given problem. STAGE 6: Visualize. Report. Present.The visualization stage can help data wrangling, analysis, and modeling stages. Data can be visualized using charts and plots suiting the characteristics of the dataset and the desired results.Visualization stage may also provide the inputs for the supply stage.STAGE 7: Supply. Products. Services.Once we are ready to monetize our data science solution or derive further return on investment from our projects, we need to think about distribution and data supply chain. This stage circles back to the acquisition stage. In fact we are acquiring data from someone else's data supply chain.

This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems. Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques. Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks. Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems. All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains. Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field.