hashintel · CiaranMn · Nov 25, 2021 · Nov 24, 2021 · Nov 24, 2021 · Nov 24, 2021
diff --git a/resources/glossary/actor-model.mdx b/resources/glossary/actor-model.mdx
@@ -3,7 +3,7 @@ objectId: 5d7d8d88-0205-4043-a46b-0ba3791f4616
 title: Actor Model
 description: "There are two main approaches to building agent-based simulations: object-oriented programming and the actor-based model."
 slug: actor-model
-tags: ["Software Development", "Simulation Modeling"]
+tags: ["Simulation Modeling", "Software Engineering"]
 ---
 
 HASH’s [hEngine](https://hash.ai/platform/engine) (which powers [hCore](https://hash.ai/platform/core) and [hCloud](https://hash.ai/platform/cloud)) is an ultra-fast framework for running large-scale simulations in a distributed fashion. To enable this, an _actor model_ is utilized, and simulations built atop HASH must take this into account. This sets HASH apart from more traditionally _object-oriented_ simulation packages.

diff --git a/resources/glossary/applicant-tracking-system.mdx b/resources/glossary/applicant-tracking-system.mdx
@@ -3,7 +3,7 @@ objectId: 49f2e3a3-40ab-4a1f-955d-d99d346cb57c
 title: Applicant Tracking System
 description: "Applicant tracking systems help employers manage recruitment and hiring."
 slug: applicant-tracking-system
-tags: ["Integrations", "Artificial Intelligence", "Enterprise Software"]
+tags: ["Business Software"]
 ---
 
 Applicant Tracking Systems are applications which help employers manage recruitment and hiring operations. They function as a single source of information, storing data submitted by the applicant as well as notes and feedback captured by the hiring and interview teams. Many ATS solutions also provide communication capabilities through the application, and will also improve the process from the applicant’s end by providing streamlined and clear interfaces for submitting resumes and other employer-requested information.

diff --git a/resources/glossary/business-intelligence.mdx b/resources/glossary/business-intelligence.mdx
@@ -3,7 +3,7 @@ objectId: d4fdddf4-1158-4746-9582-6f388d5eb377
 title: Business Intelligence
 description: "Business Intelligence allows companies to make data-driven decisions."
 slug: business-intelligence
-tags: ["Data Science", "Integrations"]
+tags: ["Business Intelligence"]
 ---
 
 Business Intelligence is a methodology which uses data collection and analysis to produce informed business decisions. It is an example of data-driven decision-making which seeks to increase revenue, improve efficiency, and provide businesses with a competitive advantage. The standard data pipeline is typically used: data is collected from both internal (sales, performance, reach) and external sources (market share, economic indicators), cleaned and prepared, then visualized or queried for insights. Visualization is often in the form of interactive dashboards which allow decision-makers to easily explore the data. Business Intelligence software can provide tooling which assists with each step in this pipeline.

diff --git a/resources/glossary/business-process-modeling.mdx b/resources/glossary/business-process-modeling.mdx
@@ -3,7 +3,7 @@ objectId: efd4a6d9-6f85-47ff-ac6d-8d1d89680202
 title: Business Process Modeling
 description: Business Process Modeling (BPM) helps organizations catalog, understand and improve their processes.
 slug: business-process-modeling
-tags: ["Simulation Modeling"]
+tags: ["Business Intelligence", "Simulation Modeling"]
 ---
 
 ## What is business process modeling?

diff --git a/resources/glossary/content-management-system.mdx b/resources/glossary/content-management-system.mdx
@@ -3,7 +3,7 @@ objectId: 4b50396d-a7a8-4fbb-a28c-d3f55dfed779
 title: Content Management System
 description: "Content management systems allow you to build and manage websites."
 slug: content-management-system
-tags: ["Integrations", "Software Development", "Enterprise Software"]
+tags: ["Business Software"]
 ---
 
 Applications used to manage web content are known as Content Management Systems. They allow users to create, manage, and modify content being displayed on a website. These are typically collaborative, allowing multiple users or teams to be responsible for their own content.

diff --git a/resources/glossary/customer-relationship-management-system.mdx b/resources/glossary/customer-relationship-management-system.mdx
@@ -3,9 +3,9 @@ objectId: 1626982c-5f49-4200-9135-833853fd1db1
 title: Customer Relationship Management System
 description: "Customer relationship management systems track and coordinate interactions between a company and its customers."
 slug: customer-relationship-management-system
-tags: ["Integrations"]
+tags: ["Business Software"]
 ---
 
 Customer relationship management (CRM) systems are applications for tracking and coordinating interactions between customers and the company. CRM systems track customers across their lifecycle, from initial contact through their purchase, onboarding, and general use. The value of a CRM comes from improving the operational handling of customer needs, and the strategic value of improving the company’s insights into customer behavior and relationships.
 
-CRMs often store some of the company’s most valuable business data - getting insight from that data and analyzing it to help management make decisions is increasingly a feature of CRMs and other applications. Simulations, powered by CRMs, can be used for [business intelligence](https://hash.ai/glossary/business-intelligence) and scenario planning.
+CRMs often store some of a company’s most valuable commercial business data - getting insight from that data and analyzing it to help management make decisions is increasingly a feature of CRMs and other applications. Simulations, powered by CRMs, can be used for [business intelligence](https://hash.ai/glossary/business-intelligence) and scenario planning (e.g. basic probabilistic forecasting, through the assignment of "likelihoods of closing" to leads).
diff --git a/resources/glossary/dag.mdx b/resources/glossary/dag.mdx
@@ -3,7 +3,7 @@ objectId: e27d8e2a-da7b-4a32-b4df-3233208fa506
 title: Directed Acyclic Graphs
 description: If you don’t know your DAGs from your dogs, you can finally get some clarity and sleep easily tonight. Learn what makes a Directed Acyclic Graph a DAG.
 slug: dag
-tags: ["Data Science"]
+tags: ["Data Science", "Graphs", "Software Engineering"]
 ---
 
 ## What is a DAG?

diff --git a/resources/glossary/data-mesh.mdx b/resources/glossary/data-mesh.mdx
@@ -3,7 +3,7 @@ objectId: bcddca8d-72a6-487f-9c11-fb910765dc2a
 title: Data Mesh
 description: "Data meshes are decentralized database solutions."
 slug: data-mesh
-tags: ["Integrations"]
+tags: ["Data Science"]
 ---
 
 A data mesh is a new paradigm for data management. Unlike existing data platforms that focus on centralizing and unifying data, a data mesh emphasizes decentralization of data control and management, with each team or domain handling and serving data products. Its analogous to a micro services architecture in web development; instead of a monolithic data warehouse serving all of the data, teams provide their own data products. The teams have ownership over the scope of the data and governance, providing them through APIs and discoverability services. The potential of data meshes is greater flexibility and a devolution of authority and responsibility to the data owners, ensuring that the teams best placed to maintain data sources do.
diff --git a/resources/glossary/data-mining.mdx b/resources/glossary/data-mining.mdx
@@ -3,7 +3,7 @@ objectId: 22e6f47e-a1b3-4c90-acfc-b18c6e486693
 title: Data Mining
 description: Data Mining is a process applied to find unknown patterns, correlations, and anomalies in data. Through mining, meaningful insights can be extracted from data.
 slug: data-mining
-tags: ["Data Science"]
+tags: ["Data Science", "Machine Learning"]
 ---
 
 ## What is Data Mining?

diff --git a/resources/glossary/deep-reinforcement-learning.mdx b/resources/glossary/deep-reinforcement-learning.mdx
@@ -3,7 +3,7 @@ objectId: 18b03d37-f394-4d18-9c0d-a579b2554a80
 title: Deep Reinforcement Learning
 description: DRL is a subset of Machine Learning in which agents are allowed to solve tasks on their own, and thus discover new solutions independent of human intuition.
 slug: deep-reinforcement-learning
-tags: ["Artificial Intelligence", "Simulation Modeling"]
+tags: ["Machine Learning", "Simulation Modeling"]
 ---
 
 ## What is DRL?

diff --git a/resources/glossary/diff.mdx b/resources/glossary/diff.mdx
@@ -3,7 +3,7 @@ objectId: bee82166-5cf3-4041-ae68-e7bf9d6c34ac
 title: Diffing
 description: Diffs are used to track changes between different versions or forks of a project, providing an overview regarding files changed, and the nature of those changes.
 slug: diff
-tags: ["Software Development"]
+tags: ["Software Engineering"]
 ---
 
 Diffs are used to track changes between two different versions or [forks](https://hash.ai/glossary/fork) of a project.

diff --git a/resources/glossary/digital-twin.mdx b/resources/glossary/digital-twin.mdx
@@ -3,7 +3,7 @@ objectId: 9e5f378b-25d4-4497-aaa4-ecb288aa27b0
 title: Digital Twin
 description: "Digital twins are a detailed simulated analogue to a real-world system"
 slug: digital-twin
-tags: ["Simulation Modeling"]
+tags: ["Business Intelligence", "Simulation Modeling"]
 ---
 
 The term digital twin (sometimes used interchangeably with "synthetic environment") refers to an in-silico, computer simulated analogue to a real-world system. It is often used to describe computer simulations that mirror real-world biological systems, architectural spaces, as well as factory/warehouses. These simulations can be used to generate [synthetic data](https://hash.ai/glossary/synthetic-data-generation), perform low-cost tradespace analyses by modifying the digital system, and generate risk assessments by introducing simulated stochastic failures, among other things.

diff --git a/resources/glossary/ego-networks.mdx b/resources/glossary/ego-networks.mdx
@@ -3,7 +3,7 @@ objectId: 8aa4e93d-7d89-4d87-8562-0fe386008a1b
 title: Ego Networks
 description: "Ego networks are a framework for local analysis of larger graphs."
 slug: ego-networks
-tags: ["Simulation Modeling"]
+tags: ["Data Science", "Graphs", "Simulation Modeling"]
 ---
 
 Egocentric networks are subsets of a larger network which provide a “local view” by assigning one central node as the ego node. These networks are also known as “perceived networks”, since they allow a large network to be analyzed from the “perspective” of some or all of its nodes, comparing the similarities and differences in local structures. Nodes connected to the “ego” are referred to as “alters”.

diff --git a/resources/glossary/enterprise-resource-planning.mdx b/resources/glossary/enterprise-resource-planning.mdx
@@ -3,15 +3,15 @@ objectId: bae0fe50-099f-4150-b9fc-c3cb36f9ad58
 title: Enterprise Resource Planning
 description: "Enterprise resource planning uses an integrated software system to manage a business' daily tasks."
 slug: enterprise-resource-planning
-tags: ["Integrations", "Artificial Intelligence"]
+tags: ["Business Software"]
 ---
 
 Enterprise Resource Planning allows businesses to manage their daily tasks through a single integrated software system. These tasks can include planning, purchasing, sales, marketing, finances, and human resources, to name a few. Each of these services may have a separate, tailored user interface, but the integration allows data to be easily shared between these different applications. A single, shared source of information reduces redundancy and data drift. ERP systems enable companies to make use of [Business Intelligence](https://hash.ai/glossary/business-intelligence) practices.
 
-- Planning: Make use of [Project Management Software](https://hash.ai/glossary/project-management-software) and link completed tasks to specific sales or purchase orders.
-- Purchasing: Make use of Internet of Things (IoT) technologies to track your inventory levels and know when to place new orders.
-- Marketing: Make use of Artificial Intelligence insights to optimize marketing strategies.
-- Finances: Automate daily tasks with [Robotic Process Automation](https://hash.ai/glossary/robotic-process-automation) workflows, and make use of real-time dashboards to understand the state of your business.
-- Human Resources: Manage an employee through recruitment, hiring, and promotion.
+- **Planning:** Make use of [Project Management Software](https://hash.ai/glossary/project-management-software) and link completed tasks to specific sales or purchase orders.
+- **Purchasing:** Make use of Internet of Things (IoT) technologies to track your inventory levels and know when to place new orders.
+- **Marketing:** Make use of [machine learning](https://hash.ai/glossary/machine-learning) insights to optimize marketing strategies.
+- **Finances:** Automate daily tasks with [Robotic Process Automation](https://hash.ai/glossary/robotic-process-automation) workflows, and make use of real-time dashboards to understand the state of your business.
+- **Human Resources:** Manage an employee through recruitment, hiring, and promotion.
 
 A key part of integration is the standardized definition of a number of data structures or schemas for use across the organization. This enables different parts of the application to easily intake and transfer data. Enterprise Resource Planning software often integrates [business intelligence](https://hash.ai/glossary/business-intelligence)-style dashboards for easily visualizing this data. Real-time data streaming makes it easy to understand the state of the company, and make informed business decisions.
diff --git a/resources/glossary/fast-healthcare-interoperability-resources.mdx b/resources/glossary/fast-healthcare-interoperability-resources.mdx
@@ -3,7 +3,7 @@ objectId: 323e9ef7-1eff-4e41-b15e-29c68e1eddf8
 title: Fast Healthcare Interoperability Resources
 description: "An electronic healthcare standard for data interoperability."
 slug: fast-healthcare-interoperability-resources
-tags: ["Integrations", "Data", "Standards"]
+tags: ["Standards"]
 ---
 
 The <a href="http://hl7.org/fhir/" target="_blank">Fast Healthcare Interoperability Resources standard</a> (FHIR) is a specification for electronic healthcare data created in 2012. Making healthcare data standardized and discoverable has been a longstanding goal of the industry, to facilitate accurate records being shared between different providers and improve [modeling]. The FHIR was created by created by the <a href="https://en.wikipedia.org/wiki/Health_Level_Seven_International" target="_blank">Health Level Seven International</a> (HL7) organization to promote semantic markup of records that work with JSON, XML, or RDF. The FHIR's conceptual primitive is the '<a href="https://www.hl7.org/fhir/resource.html" target="_blank">resource</a>':

diff --git a/resources/glossary/fork.mdx b/resources/glossary/fork.mdx
@@ -3,7 +3,7 @@ objectId: 84720a83-0797-494b-879f-49041cf09ede
 title: Forking
 description: Forking something means to create a copy of it, allowing individual developers or teams to work on their own versions of it, in safe isolation.
 slug: fork
-tags: ["Software Development"]
+tags: ["Software Engineering"]
 ---
 
 A fork is a copy of a project in HASH that has been ‘split off’ from it at a particular point in time.

diff --git a/resources/glossary/graph-databases.mdx b/resources/glossary/graph-databases.mdx
@@ -3,7 +3,7 @@ objectId: 1b1f5527-2868-4cf6-b5d2-a2b154fa4ca1
 title: Graph Databases
 description: Graph Databases are a type of database that emphasizes the relationships between data. 
 slug: graph-databases
-tags: ["Integrations", "Data Science"]
+tags: ["Graphs", "Software Engineering"]
 ---
 
 Graph Databases are a type of database that emphasizes the relationships between data. It holds data in a flexible, graph format.

diff --git a/resources/glossary/graph-representation-learning.mdx b/resources/glossary/graph-representation-learning.mdx
@@ -3,7 +3,7 @@ objectId: 5e4f377a-bfe1-4af5-ac9e-12702d07c4e5
 title: Graph Representation Learning
 description: "Graph representation learning is a more tailored way of applying machine learning algorithms to graphs and networks."
 slug: graph-representation-learning
-tags: ["Artificial Intelligence"]
+tags: ["Graphs", "Machine Learning"]
 ---
 
 

diff --git a/resources/glossary/knowledge-graph-machine-learning.mdx b/resources/glossary/knowledge-graph-machine-learning.mdx
@@ -3,7 +3,7 @@ objectId: 6b370d16-0b81-4582-91b7-277790131728
 title: Knowledge Graph Machine Learning
 description: "Knowledge graphs are information-dense inputs to machine learning algorithms, and can capture more human-readable outputs of algorithms."
 slug: knowledge-graph-machine-learning
-tags: ["Artificial Intelligence"]
+tags: ["Graphs", "Machine Learning"]
 ---
 
 Knowledge graphs have a number of features that make them desirable in the context of [Machine Learning algorithms](https://hash.ai/glossary/machine-learning). They are self-descriptive, efficient, and human-interpretable.

diff --git a/resources/glossary/knowledge-graphs.mdx b/resources/glossary/knowledge-graphs.mdx
@@ -3,7 +3,7 @@ objectId: 8e0998b0-0a8c-40a0-9c4e-c50846229443
 title: Knowledge Graphs
 description: Knowledge Graphs contextualize data and power insight generation.
 slug: knowledge-graphs
-tags: ["Artificial Intelligence", "Data Science"]
+tags: ["Data Science", "Graphs"]
 ---
 
 A knowledge graph is a collection of linked concepts. It uses a graph structure to store semantically linked entities (e.g. objects, concepts, events).

diff --git a/resources/glossary/machine-learning.mdx b/resources/glossary/machine-learning.mdx
@@ -3,7 +3,7 @@ objectId: 133cfd8b-9eea-4961-94c3-16e21614ad34
 title: Machine Learning
 description: Machine Learning is a subfield of Artificial Intelligence where parameters of an algorithm are updated from data inputs or by interacting with an environment.
 slug: machine-learning
-tags: ["Artificial Intelligence", "Data Science"]
+tags: ["Machine Learning"]
 ---
 
 ## What is Machine Learning?

diff --git a/resources/glossary/merge.mdx b/resources/glossary/merge.mdx
@@ -3,7 +3,7 @@ objectId: c4cc8423-cbef-47ef-8966-4c1af34b6470
 title: Merging
 description: Merging is the process of reconciling two projects together. In HASH merging projects is handled by submitting, reviewing and approving “merge requests”.
 slug: merge
-tags: ["Software Development"]
+tags: ["Software Engineering"]
 ---
 
 Merging is the process of reconciling two projects together. Typically in HASH, this refers to the process of merging a [forked project](https://hash.ai/glossary/fork) with the original project from which it was derived.

diff --git a/resources/glossary/metadata.mdx b/resources/glossary/metadata.mdx
@@ -3,7 +3,7 @@ objectId: 8e817ec2-2369-4001-8230-dcfee5eb6fab
 title: Metadata
 description: Metadata is data about data. It’s quite simple, really. Learn more about how it’s used within.
 slug: metadata
-tags: ["Data Science"]
+tags: ["Data Science", "Standards"]
 ---
 
 Metadata is data about data. For any piece of data, there is typically lots of metadata. Some common types of metadata found on HASH include:
@@ -19,3 +19,5 @@ Metadata is useful because it provides context to the data we use.
 Some metadata is attached automatically by systems such as HASH when users perform certain actions (e.g. creating a file, connecting a datasource, constructing a flow, or editing a row in a dataset).
 
 Other metadata can be added manually. For example, mapping data to schemas within HASH is an example of purposefully attaching metadata that describes the **type** of columns in a dataset, and the **properties** of the agents or events those columns represent.
+
+A multitude of standards exist for describing different possible and expected types of metadata, which can in turn be used by business software to provide improved functionality or interoperability.
diff --git a/resources/glossary/model-drift.mdx b/resources/glossary/model-drift.mdx
@@ -3,7 +3,7 @@ objectId: 68b25999-99bc-455a-a48e-4537390ce600
 title: Model Drift
 description: Models tend to become less accurate over time.
 slug: model-drift
-tags: ["Data Science"]
+tags: ["Data Science", "Simulation Modeling"]
 ---
 
 Model drift is the phenomenon where a model becomes less accurate, less precise, and overall less predictive over time due to changes in the relationship between the target and input variables. We can think of a model, in its most simple form, as an identified 'relationship' between two or more variables, such that a change in one causes a systematic change in another. For example, you could have a model of the relationship between temperature and ice cream sales, where when the temperature goes up so too do sales in ice cream.

diff --git a/resources/glossary/multi-agent-systems.mdx b/resources/glossary/multi-agent-systems.mdx
@@ -3,7 +3,7 @@ objectId: 64fbfcc8-2048-499c-83d5-34940c0fdcb3
 title: Multi-Agent Systems
 description: "Multi-Agent Systems represent real-world systems as collections of intelligent agents."
 slug: multi-agent-systems
-tags: ["Simulation Modeling", "Software Development"]
+tags: ["Simulation Modeling", "Software Engineering"]
 ---
 
 Multi-agent systems are computational structures made up of multiple intelligent agents who can process information, interact with their environment, and with other agents. Multi-agent systems can represent many different real-world systems: transportation, healthcare, and networking are just a few examples.

diff --git a/resources/glossary/neural-nets.mdx b/resources/glossary/neural-nets.mdx
@@ -3,7 +3,7 @@ objectId: d9ddf22d-4dfb-482b-b6c4-d28beb6d5920
 title: Artificial Neural Networks
 description: Artificial Neural Networks are computer models inspired by animal brains. They consist of collections of nodes, arranged in layers, which transfer signals.
 slug: neural-nets
-tags: ["Artificial Intelligence"]
+tags: ["Machine Learning"]
 ---
 
 ## What is an Artificial Neural Network?

diff --git a/resources/glossary/optimization-methods.mdx b/resources/glossary/optimization-methods.mdx
@@ -3,7 +3,7 @@ objectId: 68395abb-2b07-41c3-bb77-3e3b31b25edc
 title: Optimization Methods
 description: The key to finding the best solution to any problem.
 slug: optimization-methods
-tags: ["Optimization"]
+tags: ["Data Science", "Simulation Modeling"]
 ---
 
 Optimization is the practice of finding the best solution to a problem given a set of constraints. While that seems straightforward, many 'problems' are extremely complex, and can't be solved in a straightforward fashion. If there are many parameters, or if the problem space is dynamic and changes over time, it's often infeasible to brute force try every possible type of optimization. That's why there are many different optimization methods that can be employed to find good potential solutions to a problem.