Enterprise Data Management (EDM) is the basic concept of managing data in a central repository with protected access along with ways to share data responsibly. Various approaches have been pushed into a production environment with this goal in mind. These uncoordinated approaches by various segments of the organization can result in data conflicts and quality inconsistencies – making it difficult for users to trust the data as it is incorporated into models, mapped to applications, used to perform calculations, shared among participants and relied upon for decision-making. Enterprise level data search capabilities has proven to be very useful for a fast paced environment and can provide a rapid return of input that is valuable. This technology helps the end user make faster decisions based on the same information that could have taken days to receive instead of a few seconds. Many developers and companies have created a wide variety of this same concept, but over the years that concept has strayed off course. The basic concept is to create a single source search capability with the ease of use for the end user along with reliable results that pertain to the end users request in a timely manner. Now we have a large amount of software that performs similar functions each with a slightly different take on the overall objective. Operational DataTek puts the simple concept back in the forefront and pull the efforts of different communities into one management system.
BUILDING THE OBJECTIVE:
While the Operational DataTek solution was being design and conceptualized, the key fundamentals were never overlooked. Provide the user with what they need to do their job faster and more efficiently than ever before. How can this be achieved? Simple, listen to their request and learn from there needs. In order to do this the system will also have to conform to change. A dynamically changing system is what holds the key to success. If the user is searching for a specific location in an area of interest, those results will be packed with information on that item. Now the system knows the users is doing research on this particular item and will automatically let that user know when new items come in that meet the criteria either by instant message, email or notification the next time they login. We can even provide a cross reference to more information that has commonalities besides the original keywords that were used to return the result set. Now you’re getting results that are specific to your search without providing a cross reference of key words to pull back data. A basic keyword search doesn’t have to return the basic results; it should contain a rich set of content that can help the end user expand their view on the problem set. Our system provides that technology for the user.
Technical solutions designed for simplicity and security requirements working together without compromise to the purpose is our solution. Empower your analysts with a single source solution and open the door to data sharing across the board that will result in answers that didn’t exist before.
AUTOMATION, NOT PERSPIRATION!
Building a data management system that maintains itself is the ideal solution for any agency or command utilizing these tools. Even with computer technology today, a 100% automated solution isn’t realistic, but the closer you are to that percentage the lower overall cost of maintenance. We have created a system that is automated to the max. It is automated where is should be automated and manual process are kept where necessary. As new data sets are added or existing sets change, the system will adjust itself to maximize on productivity and system resources. An end user can login to the system and manage their own datasets by either uploading products or creating them on the fly. Simple data entry forms make it easy to create a standardized product. As a product is created, it can automatically be searchable by other users doing similar research. The creator of the product can manage how the document is handled, which means the end user can simply make a few changes to their settings and their products are either shared with the entire network or specific users and groups. This is data sharing at its finest. Let’s let the people who create these products share them out to who needs to know and get problems sets resolved. The automated system will share out the products created or uploaded by users based on how the user wants their information shared; there is nothing they need to do to incorporate them into any search results.
DATA, DATA & MORE DATA:
Data is the heart of the system. Our system will provide a way for users to create their own data sets and maintain them within their own profile. Along with the creation of products the user can search other user’s products as long as those users allow access. Besides the user creating and maintaining their own products, there are alternative methods to obtain data and we’re ready for it. Some data sets can be ingested automatically from locations worldwide. Either through FTP, basic file system locations or connecting directly to another database with allowed permissions.
* FTP (File Transfer Protocol) - 100% Automation
* Data starts out from the original source and is transferred to the Common File System location
* Data will be tagged and parsed and ready for final ingestion process in the System Database
* Data is now searchable by users
* File System Transfer - 50% Automation
* Data starts out from the original source and is manually dropped into a folder on the Common File System location
* Data will be tagged and parsed and ready for final ingestion process in the System Database
* Data is now searchable by users
* DB 2 DB: - 100% Automation
* The system connects to a remote database with given permissions by source owner
* Database structure of remote database is mapped and individual products are created and dropped into a folder ready for ingestion
Data will be tagged and parsed and ready for final ingestion process in the System Database
* Data is now searchable by users
DATASHARE:
Each user will be able to create and maintain their own products using a simple to use interface. Once a product is created, it will be indexed with the other datasets for optimal performance. Even though data sharing the one of the main goals, certain data sets require more secure access. These data sets will only be available to users with the right credentials. Along with individual user products created for sharing, a data set can come into the system automatically from outside sources.
DATASTREAM:
Streaming data will also be available using multiple methods including File Transfer Protocol, Drop Folders and Database Connections. The file system is automatically maintained so you don’t end up with folder names that have very little meaning to someone looking at it for the first time. The system maintains its own naming convention to keep file system organization standardized. Information moving in and out of the file system will be monitored for inconsistencies and maintain structure.
DATAPROC:
Each dataset is parsed individually with a process geared towards that particular format. Each parsing procedure is based on a common standardization format, but this can be customized for each data set that might have unique aspects that need to be exploited. This portion of the Dataflow process is extremely importation because this is where the data is optimized for search capabilities.
Data Definitions and Tagging: One of the core challenges associated with EDM is the ability to compare data that is obtained from multiple internal and external sources. In many circumstances, these sources use inconsistent terms and definitions to describe the data content itself – making it hard to compare data, hard to automate business processes, hard to feed complex applications and hard to exchange data. This frequently results in a difficult process of data mapping and cross-referencing. Normalization of all the terms and definitions at the data attribute level is referred to as the metadata component of EDM and is an essential prerequisite for effective data management.
STRUCTURE:
Each record in the database has a lot of good underlying information associated with it. As a product is ingested into the system, multiple processes are run against the product to exploit information that might have been missed while creating the original Meta record that’s associated with the product. A few items of interested are created during this process:
Full text extraction of original source
Word tagging based on content in full text extraction
XML markup of text extraction
Global variables that are populated with each record
Original product format for future reference
Metrics for each record and data set as a whole
Associated data relationships between other related products
Data Model Theory: A data model theory has three main components:
The structural part: a collection of data structures which are used to create databases representing the entities or objects modeled by the database.
The integrity part: a collection of rules governing the constraints placed on these data structures to ensure structural integrity.
The manipulation part: a collection of operators which can be applied to the data structures, to update and query the data contained in the database.
DATABASE INDIVIDUALITY:
What drives the usability of data is how data can be retrieved and used in an operational environment. Each user will be given a certain amount space in the database to host there own products. The data that has been created by a user will also be associated with other data sets that are ingested using multiple methods. Each data set holds its own data but each one also has associations built that connect the data. Running a cross reference between two individual tops has never been easier using this method. Cross referenced material is displayed so the user understands that this particular product contains data that’s related to the original request. The associated data may not contain the key words used to get them this piece of information but it’s directly related based on the key word and tagging technology.
FRONT-END USABILITY:
Using a set of tools doesn’t have to be difficult and when creating these user interfaces once should keep in mind how the end user will react to the layout, functionality of the buttons and more. A graphical interface needs to not only look good but be understood and comprehended for its intent. A user should be able to look at the screen and understand what each button does or what information is being displayed.
Read more: http://www.articlesbase.com/technology-articles/data-management-solutions-682652.html#ixzz0vACJu5z6
Under Creative Commons License: Attribution
SOURCE:
http://www.articlesbase.com/technology-articles/data-management-solutions-682652.html
BUILDING THE OBJECTIVE:
While the Operational DataTek solution was being design and conceptualized, the key fundamentals were never overlooked. Provide the user with what they need to do their job faster and more efficiently than ever before. How can this be achieved? Simple, listen to their request and learn from there needs. In order to do this the system will also have to conform to change. A dynamically changing system is what holds the key to success. If the user is searching for a specific location in an area of interest, those results will be packed with information on that item. Now the system knows the users is doing research on this particular item and will automatically let that user know when new items come in that meet the criteria either by instant message, email or notification the next time they login. We can even provide a cross reference to more information that has commonalities besides the original keywords that were used to return the result set. Now you’re getting results that are specific to your search without providing a cross reference of key words to pull back data. A basic keyword search doesn’t have to return the basic results; it should contain a rich set of content that can help the end user expand their view on the problem set. Our system provides that technology for the user.
Technical solutions designed for simplicity and security requirements working together without compromise to the purpose is our solution. Empower your analysts with a single source solution and open the door to data sharing across the board that will result in answers that didn’t exist before.
AUTOMATION, NOT PERSPIRATION!
Building a data management system that maintains itself is the ideal solution for any agency or command utilizing these tools. Even with computer technology today, a 100% automated solution isn’t realistic, but the closer you are to that percentage the lower overall cost of maintenance. We have created a system that is automated to the max. It is automated where is should be automated and manual process are kept where necessary. As new data sets are added or existing sets change, the system will adjust itself to maximize on productivity and system resources. An end user can login to the system and manage their own datasets by either uploading products or creating them on the fly. Simple data entry forms make it easy to create a standardized product. As a product is created, it can automatically be searchable by other users doing similar research. The creator of the product can manage how the document is handled, which means the end user can simply make a few changes to their settings and their products are either shared with the entire network or specific users and groups. This is data sharing at its finest. Let’s let the people who create these products share them out to who needs to know and get problems sets resolved. The automated system will share out the products created or uploaded by users based on how the user wants their information shared; there is nothing they need to do to incorporate them into any search results.
DATA, DATA & MORE DATA:
Data is the heart of the system. Our system will provide a way for users to create their own data sets and maintain them within their own profile. Along with the creation of products the user can search other user’s products as long as those users allow access. Besides the user creating and maintaining their own products, there are alternative methods to obtain data and we’re ready for it. Some data sets can be ingested automatically from locations worldwide. Either through FTP, basic file system locations or connecting directly to another database with allowed permissions.
* FTP (File Transfer Protocol) - 100% Automation
* Data starts out from the original source and is transferred to the Common File System location
* Data will be tagged and parsed and ready for final ingestion process in the System Database
* Data is now searchable by users
* File System Transfer - 50% Automation
* Data starts out from the original source and is manually dropped into a folder on the Common File System location
* Data will be tagged and parsed and ready for final ingestion process in the System Database
* Data is now searchable by users
* DB 2 DB: - 100% Automation
* The system connects to a remote database with given permissions by source owner
* Database structure of remote database is mapped and individual products are created and dropped into a folder ready for ingestion
Data will be tagged and parsed and ready for final ingestion process in the System Database
* Data is now searchable by users
DATASHARE:
Each user will be able to create and maintain their own products using a simple to use interface. Once a product is created, it will be indexed with the other datasets for optimal performance. Even though data sharing the one of the main goals, certain data sets require more secure access. These data sets will only be available to users with the right credentials. Along with individual user products created for sharing, a data set can come into the system automatically from outside sources.
DATASTREAM:
Streaming data will also be available using multiple methods including File Transfer Protocol, Drop Folders and Database Connections. The file system is automatically maintained so you don’t end up with folder names that have very little meaning to someone looking at it for the first time. The system maintains its own naming convention to keep file system organization standardized. Information moving in and out of the file system will be monitored for inconsistencies and maintain structure.
DATAPROC:
Each dataset is parsed individually with a process geared towards that particular format. Each parsing procedure is based on a common standardization format, but this can be customized for each data set that might have unique aspects that need to be exploited. This portion of the Dataflow process is extremely importation because this is where the data is optimized for search capabilities.
Data Definitions and Tagging: One of the core challenges associated with EDM is the ability to compare data that is obtained from multiple internal and external sources. In many circumstances, these sources use inconsistent terms and definitions to describe the data content itself – making it hard to compare data, hard to automate business processes, hard to feed complex applications and hard to exchange data. This frequently results in a difficult process of data mapping and cross-referencing. Normalization of all the terms and definitions at the data attribute level is referred to as the metadata component of EDM and is an essential prerequisite for effective data management.
STRUCTURE:
Each record in the database has a lot of good underlying information associated with it. As a product is ingested into the system, multiple processes are run against the product to exploit information that might have been missed while creating the original Meta record that’s associated with the product. A few items of interested are created during this process:
Full text extraction of original source
Word tagging based on content in full text extraction
XML markup of text extraction
Global variables that are populated with each record
Original product format for future reference
Metrics for each record and data set as a whole
Associated data relationships between other related products
Data Model Theory: A data model theory has three main components:
The structural part: a collection of data structures which are used to create databases representing the entities or objects modeled by the database.
The integrity part: a collection of rules governing the constraints placed on these data structures to ensure structural integrity.
The manipulation part: a collection of operators which can be applied to the data structures, to update and query the data contained in the database.
DATABASE INDIVIDUALITY:
What drives the usability of data is how data can be retrieved and used in an operational environment. Each user will be given a certain amount space in the database to host there own products. The data that has been created by a user will also be associated with other data sets that are ingested using multiple methods. Each data set holds its own data but each one also has associations built that connect the data. Running a cross reference between two individual tops has never been easier using this method. Cross referenced material is displayed so the user understands that this particular product contains data that’s related to the original request. The associated data may not contain the key words used to get them this piece of information but it’s directly related based on the key word and tagging technology.
FRONT-END USABILITY:
Using a set of tools doesn’t have to be difficult and when creating these user interfaces once should keep in mind how the end user will react to the layout, functionality of the buttons and more. A graphical interface needs to not only look good but be understood and comprehended for its intent. A user should be able to look at the screen and understand what each button does or what information is being displayed.
Read more: http://www.articlesbase.com/technology-articles/data-management-solutions-682652.html#ixzz0vACJu5z6
Under Creative Commons License: Attribution
SOURCE:
http://www.articlesbase.com/technology-articles/data-management-solutions-682652.html
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