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Modeling temporal aspects of sensor data for Mongo. DB No. SQL database. The emergence of Web 2. Internet of Things (Io. T) and millions of users have played a vital role to build a global society, which generates volumes of data.

At the same time, this data tsunami has threatened to overwhelm and disrupt the data centers [1]. Due to this constant data growth the information storage, support and maintenance have become a challenge while using the traditional data management approaches, such as structural relational databases. To support the data storage demands of new generation applications, the distributed storage mechanisms are becoming the de- facto storage method [2]. Scaling can be achieved in two ways, vertical or horizontal, where the former means adding up resources to a single node, whereas in the latter case we add more nodes to the system [3]. For the problems that have arisen due to data proliferation, the RDBMS fail to scale the applications horizontally according to the incoming data traffic [2]; because they require data replication on multiple nodes, so they are not flexible to allow data and read/write operations distributed over many servers.

So we need to find systems that would be able to manage big volumes of data. This flood of data passes challenges not only due to its sheer size but also due to the data types, hence demands more robust mechanisms to tackle different data formats.

The Web, e- science solutions, sensor laboratories and industrial sector produce in abundance both structural, semi- structural and non- structural data [4, 5, 6]. This is not a new problem, and can be traced back to the history of object- relational databases, under the name of Object- Relational Impedance Mismatch [7]. This mismatch is natural, when we try to model an object into a fixed relational structure. Similarly, the digital information with different structures, such as natural text, PDF, HTML and embedded systems data, is not simple enough to capture as entities and relationships [8]. Even if we manage to do this, it will not be easy to change afterwards, hence such mechanisms are rigid for schema alteration because they demand pre- schema definition techniques. Several new generation systems do not like to fix their data structure to a single schema; rather they want their schema to evolve in parallel to an entity data type’s adaptation, hence they want flexibility [9, 1. Besides the data abundance and different formats, the rapid flow of data has also attracted the researchers to find mechanisms to manage the data in motion.

Typically this is to consider that, how quickly the data is produced and stored, and its associated rates of retrieval and processing. This idea of data in motion is evoking far more interest than the conventional definitions, and needs a new way of thinking to solve the problem [1.

This is not associated only with the growth rate at the data acquisition end, but also data- flow rate during transmission; as well the speed at which data is processed and stored in the data repositories. Any way, we are aware of the fact that today’s enterprises have to deal with petabytes instead of terabytes; and the increase in smart object technology alongside the streaming information has led the constant flow of data at a pace that has threatened the traditional data management systems [1. RDBMS use two- dimensional tables to represent data and use multi- join transactional queries for the database consistency. Although they are mature and still useful for many applications, but processing of volumes of data using multi- joins is prone to performance issues [1.

This problem is evident when extensive data processing is required to find hidden useful information in huge data volumes; but such data mining techniques are not in our current focus [1. No. SQL temporal modeling and schema based data integration. Above discussed problems of prolific, multi- structured heterogeneous data in flow urge the researchers to conduct research to find alternate data management mechanisms, hence No. SQL data management systems have appeared and are now becoming a standard to cope with big data problems [1.

Such new data management systems are being used by many companies, such as Google, Amazon etc. The four primary categories of their data model are: (i) key- value stores, (ii) column- oriented, (iii) document, and (iv) graph databases [1. For rationality, sanity and demonstrating the storage structure, the researchers follow the database schema techniques without losing the advantages of schema flexibility provided by No.

SQL databases. Such schema modeling strategies in No. SQL databases are quite different from the relational databases.

Collections, normalization and document embedding are few variants to consider during building schema models because they affect the performance and storage effectively because such databases grow very quickly. While dealing with real- time data, in continuous or sliced snapshot data streams, the data items possess observations which are ordered over time [2. During previous years, research efforts had been conducted to capture temporal aspects in the form of data models and query languages [2. But mostly those efforts were for relational or object- oriented models [2.

The emerging applications, such as sensor data [2. Internet traffic [2.

The current methods of centralized or distributed storage with static data impose constraints in addressing the real- time requirements [3. They have limited features to support the latest data stream challenges and demand research to augment the existing technologies [3.

In remote healthcare long term monitoring operations, based on Body Area Networks (BAN), demand low energy consumption due to limited memory, processing and battery resources [3. These systems also demand communication and data interoperability among sensor devices [3. Recently a propriety protocol ANT+ provides these low energy consumption features; and strengthens the goals of Io. T through the interoperability of devices based on Machine- to- Machine (M2. M) mechanisms, which employs use case specific device profile standards [3. Device interoperability, low energy and miniaturisation features allow the building of large ecosystems, hence enable millions of vendor devices to get integrated and interoperated.

Io. T ecosystems want general storage mechanisms having structural flexibility to accept different data formats arriving from millions of sensory objects [3. The non- relational or No.

SQL databases are schema- free [2]; and allow storage of different data formats without prior structural declarations [3. However for the storage we need to investigate the No. SQL models to design and develop [8, 2. Although all No. SQL databases have unique advantages, but document- oriented storage, as Mongo. DB provides, is considered robust for handling multiple structural information to support Io.

T goals [3. 8]. This rejects the relational structural storage and favours Java Script Object Notations (JSON) documents to support dynamic schemas; hence provide integration to different data types besides scalability features [3. This article presents a general approach to model temporal aspects of ANT+ sensor data. The authors develop a prototype for the Mongo. DB No. SQL real- time platform and discuss the temporal data modeling challenges and decisions. An algorithm is presented which integrates JSON data as hierarchical documents and evolves the proposed schema without loosing flexibility and scalability.

This article is organized as follows. Data stream and data stream management systems (DSMS)" is about time series data. Different No. SQL databases are discussed in detail in "Limitations of RDBMS". It is followed by a subsection discussing Mongo. DB as a well- known document oriented database. Big data management frameworks", discusses the different techniques to model time series data using Mongo.