Multiterm Keyword Search in NoSQL Systems (original) (raw)
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Computing Research Repository, 2011
NoSQL systems are more and more deployed as back-end infrastructure for large-scale distributed online platforms like Google, Amazon or Facebook. Their applicability results from the fact that most services of online platforms access the stored data objects via their primary key. However, NoSQL systems do not efficiently support services referring more than one data object, e.g. the term-based search for data objects. To address this issue we propose our architecture based on an inverted index on top of a NoSQL system. For queries comprising more than one term, distributed indices yield a limited performance in large distributed systems. We propose two extensions to cope with this challenge. Firstly, we store index entries not only for single term but also for a selected set of term combinations depending on their popularity derived from a query history. Secondly, we additionally cache popular keys on gateway nodes, which are a common concept in real-world systems, acting as interface for services when accessing data objects in the back end. Our results show that we can significantly reduces the bandwidth consumption for processing queries, with an acceptable, marginal increase in the load of the gateway nodes.
NoSQL : A New Horizon in Big Data
This Big data is useful for data sets where their size or type is away from the capability of traditional relational databases for capturing, managing and processing the data with low-latency. Relational databases were not designed to cope with the scale and agility challenges that face modern applications, nor were they built to take advantage of the commodity storage and processing power available today. NoSQL encompasses a wide variety of different database technologies that were developed in response to the demands presented in building modern applications. In this paper collection of NoSQL database tools are illustrated and also compared with the salient features.
—The rising interest in NoSQL technology over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies From survey we create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use from the software engineer point of view.
Do NoSQL Databases Cope with Current Data Challenges
Data is growing very rapidly and becoming more complex in variety, velocity and volume. The notion of big data is associated with opportunities as well as challenges for existing computing techniques. Traditional data management tools and techniques are typically designed for structured data management and not sufficiently process and analyze large data volumes. NoSQL provides set of storage alternatives with the various characteristics that are intended to manage and process big data. There are different types of NoSQL stores such as Key-Value, Document-Oriented, Column-Oriented and Graph-Oriented. It is necessary to set an evaluation criteria for these databases. This work presents an evaluation criteria for existing NoSQL stores cope with the current data challenges.
A review on NoSQL: Applications and challenges
International Journal of Advanced Research in Computer Science, 2017
Now a day the technology is growing rapidly stimulating and generating whopping amount of data. Every day people and companies generate huge amounts of data and this data may be unstructured, semi-structured and structured. That’s why we need to design databases which can store this type of data in huge volumes. The name of this database is NoSQL databases. NoSQL database solves this type of problems. NoSQL database is being used widely and it is a commonly known as engines well scale. Therefore, it is useful to investigate how different factors, such as workload, data size and number of simultaneous sessions influence scaling capabilities. In this paper we describe the brief introduction of NoSQL and its categories and also what the benefits of NoSQL are and why we are using now. Keywords: NoSQL, Graph DB, Key value DB, Column DB, Document DB
NoSQL Systems for Big Data Management
—The advent of Big Data created a need for out-of-the-box horizontal scalability for data management systems. This ushered in an array of choices for Big Data management under the umbrella term NoSQL. In this paper, we provide a taxonomy and unified perspective on NoSQL systems. Using this perspective, we compare and contrast various NoSQL systems using multiple facets including system architecture, data model, query language, client API, scalability, and availability. We group current NoSQL systems into seven broad categories: Key-Value, Table-type/Column, Document, Graph, Native XML, Native Object, and Hybrid databases. We also describe application scenarios for each category to help the reader in choosing an appropriate NoSQL system for a given application. We conclude the paper by indicating future research directions .