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Network Working Group                                         M. McBride
Internet-Draft                                                 Futurewei
Intended status: Standards Track                             D. Kutscher
Expires: January 11, 2021                               Emden University
                                                             E. Schooler
                                                           CJ. Bernardos
                                                                D. Lopez
                                                          Telefonica I+D
                                                           July 10, 2020

                    Data Discovery Problem Statement


   If data is the new oil of the 21st century, then we need a
   standardized way of locating, capturing, classifying and transforming
   this raw data to generate insights and recommendations.  Data, like
   oil, needs to be discovered and captured in order to be refined and
   valuable.  While the topic of data discovery can be far reaching,
   this document focuses on the problem of actually locating data,
   throughout a network of data servers, in a standardized way.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on January 11, 2021.

Copyright Notice

   Copyright (c) 2020 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   2
   2.  Problem Scope . . . . . . . . . . . . . . . . . . . . . . . .   2
   3.  Existing Solutions  . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  Proprietary . . . . . . . . . . . . . . . . . . . . . . .   3
     3.2.  Opensource  . . . . . . . . . . . . . . . . . . . . . . .   4
   4.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   4
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   5
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   5
   7.  Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .   5
   8.  Normative References  . . . . . . . . . . . . . . . . . . . .   5
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   6

1.  Introduction

   There are myriad proprietary and standardized ways of discovering
   networking devices and hosts.  There are many solutions for
   discovering data within a database.  There are proprietary, non-
   standardized, ways of discovering the data that may be stored
   throughout an environment of networking devices.  We can discover
   information about the devices but can't locate and capture stored
   data in a standard way.  With more networking devices storing
   collected data there needs to be a standard way of discovering the
   specific data needed amongst a potentially huge lake of databases.

1.1.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in RFC 2119 [RFC2119].

2.  Problem Scope

   Data may be cached, copied and/or stored at multiple locations in the
   network on route to its final destination.  With an increasing
   percentage of devices connecting to the Internet being mobile,

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   support for in-the-network caching and replication is critical for
   continuous data availability.  There are data repositories throughout
   a modern network and there needs to be a standardized way to locating
   the repositories and discovering the desired data within.

   There are many types of relational (SQL) and non-relational (NoSQL)
   data classification solutions.  Existing database classification
   engines allow for scanning of a database.  We are defining the
   problem, however, of having a standards based solution to discover
   first where the databases exist throughout a network and then where
   specific data objects are located.

   Data discovery is likely to look different depending on if we are
   seeking global vs local discovery.  Data discovery may be location-
   driven.  A standard to find data may want to search for it in a more
   proximal fashion, i.e., find the data that matches the search that is
   nearest to a location.

   There is so much data being created, processed, and migrated, that it
   may only sometimes get stored more permanently in a database.  There
   is going to be slightly less permanent data that resides for a time
   in memory, so that it may be discovered and accessed quickly.  It may
   be more dynamic and short lived.  Although we refer to the data store
   as a database, it may reside entirely in memory, and/or it may be
   stored in some other non-SQL indexing technology.

   Each database essentially provides a directory service for the data
   within them and that directory service can be viewed as metadata.
   There is the need to understand where the databases/data lakes/
   pockets of data reside.  The location of each data store is the first
   level discovery problem, and the details of the database's directory
   is the second level discovery problem.

   Publish and subscribe approaches allow nodes to express their
   interest in specific pieces of data without knowing the location of
   the data.  There might be sources of data to be discovered that might
   not produce the specific data desired by the subscribers (or not
   produce data with a specific format or frequency).  The subscriber
   will want to find the publishers which send the desired data

3.  Existing Solutions

3.1.  Proprietary

   There are many existing proprietary database discovery solutions we
   can evaluate in order to understand what aspects we need to
   standardized.  For instance there is IBM Cognos, Wipro Data Discovery

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   Platform (DDP), and Amazon Macie among many others.  Macie, for
   instance, is a data security and data privacy service that uses
   machine learning and pattern matching to discover and protect data in
   AWS.  The service allows you to define data types in order to
   discover and protect the data that may be unique to a use case.

3.2.  Opensource

   There are opensource data solutions such as from ScienceBase
   (https://sciencebase.usgs.gov/).  The U.S.  Geological Survey (USGS)
   is developing ScienceBase, an open source, collaborative, scientific
   data and information management platform.  It provides current
   documentation about its structure, information model, services,
   directory and repository. sbtools uses an R (command line driven
   program used to find data within the platform) interface for

   Another solution is the Interplanetary File System (ipfs.io).  IPFS
   is a distributed system for storing and accessing files, websites,
   applications, and data.  IPFS is a peer-to-peer (p2p) storage
   network.  Content is accessible through peers, located anywhere in
   the world, that might relay information, store it, or do both.  IPFS
   knows how to find what you ask for via its content address, rather
   than its location.  There are three fundamental principles to
   understanding IPFS:

   o  Unique identification via content addressing

   o  Content linking via directed acyclic graphs (DAGs)

   o  Content discovery via distributed hash tables (DHTs)

4.  Use Cases

   Here are some of the use cases which will benefit from standards
   based data discovery solutions:

   o  We need a standards based solution to discover the increasing
      amount of data being stored in various locations throughout a
      network including at the edge.  We need a standard protocol set
      for doing this data discovery, on the device or infrastructure
      edge, in order to meet the requirements of many use cases.  We
      will have terabytes of data on the edge and need a way to identify
      its existence and find the desired data.
      [I-D.mcbride-edge-data-discovery-overview] is focusing on this
      aspect of data discovery.

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   o  We need a secure standards based solution for data discovery.
      Several of the proprietary secure data discovery solutions use
      machine learning and pattern matching to discover and protect the
      data.  We need to incorporate existing, or new, ietf security
      solutions when discoverying data.

   o  We need a standards based solution for using named based solutions
      for data discovery.  An Information Centric Networking (ICN)
      enabled network routes data by name (vs address), caches content
      natively in the network, and employs data-centric security.  Data
      discovery may require that data be associated with a name or
      names, a series of descriptive attributes, and/or a unique
      identifier.  NDN (Named Data Networking) can be applied to edge
      data discovery to make it much easier to extract data and meta-
      data by naming it.  If data was named we would be able to discover
      the appropriate data simply by its name.

   o  We need a standards based way of discovering data in mobile
      wireless networks.  Data could reside on the eNodeB or other
      wireless access infrastructure equipment in addition to residing
      on servers in the packet core.

5.  IANA Considerations


6.  Security Considerations

   Data and metadata discovery are both a function of who asks for the
   data and in what context.  The policies attached to the database and
   the metadata are going to dictate what view into the data that the
   system returns to the requester.

7.  Acknowledgement

8.  Normative References

              McBride, M., Kutscher, D., Schooler, E., and C. Bernardos,
              "Edge Data Discovery for COIN", draft-mcbride-edge-data-
              discovery-overview-03 (work in progress), January 2020.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

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Authors' Addresses

   Mike McBride

   Email: michael.mcbride@futurewei.com

   Dirk Kutscher
   Emden University

   Email: ietf@dkutscher.net

   Eve Schooler

   Email: eve.m.schooler@intel.com
   URI:    http://www.eveschooler.com

   Carlos J. Bernardos
   Universidad Carlos III de Madrid
   Av. Universidad, 30
   Leganes, Madrid  28911

   Phone: +34 91624 6236
   Email: cjbc@it.uc3m.es
   URI:   http://www.it.uc3m.es/cjbc/

   Diego R. Lopez
   Telefonica I+D
   Don Ramon de la Cruz, 82
   Madrid  28006

   Email: diego.r.lopez@telefonica.com

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