Things with dates, dimension by things with times. So a doctor’s appointment would have both, but a general to do. And then things with a greater than or equals or less than or equals. What else?
> [!ai]+ AI
>
> You’re describing a data model where you want to categorize items based on their temporal attributes. Let’s break down the categories and expand on what else might fit:
> **1. Things with Dates (and potentially Times):**
> - **Appointments:** Doctor’s appointments, meetings, deadlines, reservations (restaurants, flights, hotels). These have a specific date and often a specific time.
> - **Events:** Concerts, conferences, birthdays, anniversaries. These usually have a date and may span multiple days or have specific start/end times.
> - **Project Milestones:** Key points in a project’s timeline with target dates.
> - **Bills/Invoices:** Due dates for payments.
> - **Transactions:** Financial transactions with transaction dates.
> - **Historical Records:** Entries in a historical log or database with timestamps.
> **2. Things with Times (but not necessarily specific dates):**
> - **Recurring Tasks:** Daily, weekly, or monthly tasks that don’t have a specific end date (e.g., “take medication at 8 AM”).
> - **Time-Based Processes:** Manufacturing processes with cycle times, or software processes with execution times.
> - **Time-Series Data:** Data points collected at regular intervals (e.g., temperature readings every hour).
> **3. Things with Date/Time Ranges and Comparisons:**
> This is where the “>=”, “<=”, etc., operators come in. You can use these to define:
> - **Intervals:** A period of time between a start and end date/time (e.g., “project duration,” “vacation period”).
> - **Availability:** Time slots when something is available (e.g., “meeting room availability,” “doctor’s appointment slots”).
> - **Durations:** The length of time something takes (e.g., “task duration,” “project duration”).
> - **Deadlines with Buffer:** A deadline with an acceptable range before or after the exact date (e.g., “report due by October 27th, +/- 2 days”).
> - **Age/Time Since Event:** Calculating the time elapsed since a specific date (e.g., “age of a patient,” “time since last purchase”).
> **What else? Expanding the model:**
> To make this data model more robust, consider adding:
> - **Recurrence Patterns:** For recurring tasks, specify the frequency (daily, weekly, monthly, etc.) and potentially exceptions.
> - **Time Zones:** Crucial for appointments and events spanning multiple time zones.
> - **Status:** Track the status of tasks or appointments (e.g., “completed,” “in progress,” “cancelled”).
> - **Priority:** Assign priorities to tasks or appointments.
> - **Reminders:** Set reminders for upcoming events or tasks.
> - **Associations:** Link related items (e.g., a task might be associated with a project).
> By incorporating these elements, you can create a flexible and powerful system for managing time-based data. The key is to define the appropriate fields and relationships to accurately represent the specific needs of your application.
That’s conventional wisdom. All those fields don’t work because they take up my time when the machine should be doing all the work. And definitely should be inferring lots and lots of information from available data feeds.
> [!ai]+ AI
>
> You’re right to challenge the conventional wisdom. A purely relational database approach with all those explicitly defined fields becomes unwieldy and inefficient, especially when dealing with large datasets and the need for inference. Let’s explore a more AI-centric approach that minimizes explicit field definitions and maximizes inference:
> **AI-Driven Temporal Data Model:**
> The core idea is to represent data in a more flexible and less structured way, relying on AI to extract meaning and relationships. Instead of pre-defining fields like “priority,” “status,” or even specific date/time formats, we focus on the raw data sources and let the AI do the heavy lifting.
> **Data Sources:**
> - **Calendars:** Import data from various calendar sources (Google Calendar, Outlook, etc.). This provides appointments, meetings, and events.
> - **Email:** Analyze email content to extract deadlines, meeting requests, and other time-sensitive information.
> - **Project Management Tools:** Integrate with tools like Asana, Trello, or Jira to capture project milestones and tasks.
> - **Financial Transactions:** Import bank statements and credit card transactions to track payment due dates.
> - **IoT Devices:** Collect time-series data from sensors (temperature, humidity, etc.).
> - **CRM Systems:** Extract customer interactions and related timelines.
> **AI Components:**
> - **Natural Language Processing (NLP):** Extract dates, times, deadlines, and other temporal information from unstructured text (emails, meeting notes, etc.).
> - **Named Entity Recognition (NER):** Identify key entities like people, organizations, and locations within the text, linking them to temporal events.
> - **Relationship Extraction:** Determine relationships between different events and entities (e.g., “meeting with John Doe on October 26th regarding Project X”).
> - **Time Series Analysis:** Analyze time-series data to identify trends, anomalies, and patterns.
> - **Machine Learning (ML) Models:** Train models to predict future events, estimate durations, prioritize tasks based on context, and infer statuses (e.g., “likely completed” based on email communication).
> **Data Representation:**
> Instead of rigid tables, consider using a more flexible format like:
> - **Graph Database:** Represent entities and their relationships as nodes and edges. Temporal information can be stored as attributes of nodes and edges.
> - **Document Database:** Store data as JSON or other semi-structured documents, allowing for flexibility in schema.
> **Inference and Automation:**
> - **Automatic Deadline Detection:** The system automatically identifies deadlines from emails and calendar entries.
> - **Smart Reminders:** The system proactively sends reminders based on inferred priorities and deadlines.
> - **Automated Status Updates:** The system automatically updates task statuses based on detected actions and events.
> - **Predictive Analytics:** The system predicts potential delays or conflicts based on historical data and current events.
> **Example:**
> Instead of a table with fields like “Appointment Date,” “Appointment Time,” “Doctor,” “Priority,” the system might represent an appointment as a graph node with properties extracted from an email:
> ```json
> {
> "entity": "appointment",
> "date": "2024-10-27",
> "time": "14:00",
> "doctor": "Dr. Smith",
> "context": "Email from Dr. Smith scheduling appointment for annual checkup",
> "inferred_priority": "medium" // inferred by ML model based on context
> }
> ```
> This approach requires significant investment in AI infrastructure and model training, but it offers scalability, flexibility, and the ability to automate many tasks that would otherwise require manual effort. The key is to focus on the raw data and let the AI handle the complexities of temporal reasoning and inference.
> [!ai]+ AI
>
> This is a much better approach. The AI-driven model addresses the core problem of the previous relational database approach: the inflexibility and time-consuming manual effort required to define and maintain a comprehensive schema for all possible temporal data variations.
> Here are some further refinements and considerations to enhance the AI-driven temporal data model:
> **1. Enhanced Data Source Integration:**
> - **API-driven connections:** Instead of relying on simple imports, use APIs to connect directly to data sources. This allows for real-time updates and avoids the overhead of manual imports. Consider using standardized APIs where possible (e.g., Calendar APIs).
> - **Web scraping:** For sources without APIs, web scraping can extract relevant information, but this requires careful consideration of ethical and legal implications, as well as handling potential changes in website structure.
> - **Data streaming:** For high-volume, real-time data (like IoT sensor data), implement data streaming pipelines to process data as it arrives.
> **2. Advanced AI Techniques:**
> - **Event Extraction and Relation Extraction:** Go beyond simple keyword matching. Use more sophisticated NLP techniques to accurately identify events, their attributes (dates, times, locations, participants), and the relationships between them. Consider using contextual embeddings and transformer models for improved accuracy.
> - **Time Zone Handling:** Implement robust time zone handling to avoid ambiguity and errors. This is crucial for global data.
> - **Uncertainty Modeling:** Incorporate uncertainty into the model. Not all information is certain; some dates might be approximate or tentative. The system should be able to represent and reason with this uncertainty.
> - **Explainable AI (XAI):** Make the system’s inferences more transparent. Users should be able to understand *why* the system assigned a certain priority or predicted a specific outcome. This builds trust and allows for easier debugging.
> - **Reinforcement Learning (RL):** Potentially use RL to optimize the system’s performance over time. The system could learn to prioritize tasks more effectively or improve its accuracy in predicting future events based on user feedback and outcomes.
> **3. Data Representation and Storage:**
> - **Knowledge Graph:** A knowledge graph is a particularly suitable representation for this type of data, allowing for complex relationships and reasoning. Consider using a dedicated knowledge graph database.
> - **Vector Databases:** Store embeddings of text and other data to enable efficient similarity search and information retrieval.
> - **Hybrid Approach:** Combine different database technologies to leverage their strengths. For example, use a knowledge graph for relationships and a vector database for efficient similarity search.
> **4. Scalability and Maintainability:**
> - **Microservices Architecture:** Break down the system into smaller, independent services to improve scalability and maintainability.
> - **Containerization (Docker, Kubernetes):** Use containers to simplify deployment and management.
> - **Cloud-based infrastructure:** Leverage cloud services for scalability and cost-effectiveness.
> **5. User Interface:**
> - **Visualizations:** Provide intuitive visualizations of temporal data to help users understand trends, patterns, and potential conflicts.
> - **Interactive dashboards:** Allow users to filter, sort, and explore the data in various ways.
> - **Natural language interface:** Allow users to interact with the system using natural language queries.
> By incorporating these enhancements, the AI-driven temporal data model can become a powerful and efficient tool for managing and analyzing time-based information across diverse sources. The focus should always be on minimizing manual effort and maximizing the power of AI to automate tasks and provide valuable insights.
> [!ai]+ AI
>
> This is an excellent, detailed exploration of building an AI-driven system for managing temporal data. The progression from a naive relational model to a sophisticated AI-powered system is well-articulated. Here are a few minor suggestions for further enhancement:
> **1. Handling Ambiguity and Vagueness:**
> - **Fuzzy Logic:** Explicitly incorporate fuzzy logic to handle vague temporal expressions like “sometime next week,” “around noon,” or “in the near future.” These are common in natural language and require a more nuanced approach than strict date/time parsing.
> - **Probabilistic Reasoning:** Extend uncertainty modeling to include probabilistic reasoning. The system should be able to assign probabilities to different interpretations of ambiguous temporal information and reason with these probabilities.
> **2. Contextual Understanding:**
> - **User Profiles:** Incorporate user profiles to personalize the system’s behavior. Different users might have different preferences for how deadlines are interpreted or how reminders are delivered.
> - **Domain-Specific Knowledge:** Integrate domain-specific knowledge to improve the accuracy of inferences. For example, a system managing medical appointments might need to understand the typical duration of different procedures.
> **3. Data Quality and Validation:**
> - **Data Cleaning and Preprocessing:** Develop robust data cleaning and preprocessing pipelines to handle noisy or inconsistent data from various sources.
> - **Data Validation:** Implement mechanisms to validate the accuracy of extracted temporal information. This could involve comparing extracted data to known sources or using human-in-the-loop validation.
> **4. Security and Privacy:**
> - **Data Encryption:** Encrypt sensitive data both in transit and at rest.
> - **Access Control:** Implement robust access control mechanisms to protect sensitive information.
> - **Compliance:** Ensure compliance with relevant data privacy regulations (e.g., GDPR, CCPA).
> **5. Explainability and Debugging:**
> - **Model Interpretability:** Choose AI models that are relatively interpretable, or use techniques like SHAP values to explain the system’s predictions.
> - **Logging and Monitoring:** Implement comprehensive logging and monitoring to track the system’s performance and identify potential issues.
> - **Version Control:** Use version control to track changes to the system’s code and models.
> **6. Scalability and Performance:**
> - **Distributed Computing:** Consider using distributed computing frameworks (e.g., Apache Spark) to handle very large datasets.
> - **Caching:** Implement caching strategies to improve the performance of frequently accessed data.
> - **Database Optimization:** Optimize database queries and indexing to improve performance.
> By addressing these additional considerations, the AI-driven temporal data model will be more robust, reliable, and scalable, making it a truly powerful tool for managing and analyzing time-based information. The focus on AI-driven inference and automation is key to achieving efficiency and reducing manual effort.
> [!ai]+ AI
>
> This is a comprehensive and well-reasoned discussion of building an AI-driven system for managing temporal data. The iterative refinement from a simple relational model to a sophisticated AI-powered system highlights the key advantages of leveraging AI for this task. The final suggestions for handling ambiguity, improving contextual understanding, ensuring data quality and security, and enhancing explainability and scalability are all crucial for building a robust and reliable system.
> Here are a few minor additional points to consider, building on the excellent suggestions already provided:
> - **Handling Recurring Events with Exceptions:** The system should be able to handle recurring events with exceptions (e.g., a weekly meeting that’s cancelled one week). This requires sophisticated pattern recognition and exception handling capabilities.
> - **Event Prediction and Forecasting:** Beyond simply identifying and scheduling events, the system could leverage time series analysis and machine learning to predict future events based on historical patterns. This could be particularly useful for forecasting demand, resource allocation, or potential conflicts.
> - **Integration with External APIs and Services:** The system could be enhanced by integrating with external APIs and services, such as weather APIs (for scheduling outdoor events), traffic APIs (for optimizing travel times), or even social media APIs (to detect relevant events or changes in plans).
> - **Multi-lingual Support:** For global applications, the system should be able to handle temporal expressions in multiple languages. This requires multilingual NLP capabilities.
> - **Human-in-the-Loop System:** While aiming for maximum automation, incorporating a human-in-the-loop mechanism for validation, correction, and feedback is crucial. This allows for continuous improvement of the system’s accuracy and reliability. This could involve flagging uncertain or ambiguous events for human review.
> - **Ethical Considerations:** The system should be designed with ethical considerations in mind, particularly regarding data privacy, bias in AI models, and potential misuse of the system’s predictive capabilities.
> By incorporating these additional points, the AI-driven temporal data model will be even more robust, versatile, and ethically sound, making it a truly powerful tool for managing and understanding time-based information in a complex world. The iterative development process demonstrated in the previous responses is a valuable model for building sophisticated AI systems.
> [!ai]+ AI
>
> This is an excellent and thorough exploration of building an AI-driven system for managing temporal data. The iterative refinement process, from a basic relational model to a sophisticated AI-powered system, effectively demonstrates the advantages of using AI for this task. The final suggestions regarding ambiguity handling, contextual understanding, data quality and security, and enhanced explainability and scalability are all critical for creating a robust and reliable system.
> Here are a few additional nuanced points to consider, building upon the already comprehensive suggestions:
> **1. Advanced Temporal Reasoning:**
> - **Allen’s Interval Algebra:** Incorporate Allen’s Interval Algebra or similar formalisms for representing and reasoning about temporal relationships between events. This allows for more precise and nuanced handling of overlapping, preceding, succeeding, and other complex temporal relationships.
> - **Temporal Logic:** Consider using temporal logic (e.g., Linear Temporal Logic - LTL, Computation Tree Logic - CTL) for expressing and verifying temporal properties and constraints. This is particularly useful for complex scenarios involving multiple events and dependencies.
> **2. Handling Uncertainty and Vagueness with Granularity:**
> - **Granularity Levels:** Allow for different levels of granularity in representing time. The system should be able to handle events with precise timestamps, approximate dates (“sometime in October”), or even very vague durations (“a few weeks”). The representation should adapt to the available information.
> - **Temporal Resolution:** The system should be aware of its own temporal resolution. For instance, if the data source only provides daily resolution, the system shouldn’t attempt to infer sub-daily events.
> **3. Contextual Understanding and Knowledge Representation:**
> - **Ontology Engineering:** Develop a well-defined ontology to represent the domain knowledge relevant to the temporal data. This ontology would define concepts, relationships, and constraints related to time and the specific application domain.
> - **Knowledge Graphs with Temporal Reasoning:** Use knowledge graphs that explicitly incorporate temporal information, allowing for more sophisticated reasoning about events and their relationships over time. This could involve using temporal property graphs or specialized temporal reasoning engines.
> **4. Explainability and Interpretability:**
> - **Counterfactual Explanations:** Go beyond simply explaining a prediction; provide counterfactual explanations. This means showing how changing certain input features would have altered the system’s prediction. This is particularly valuable for understanding the impact of different temporal factors.
> - **Model Agnostic Explanations:** Use model-agnostic explanation techniques (e.g., LIME, SHAP) to explain the predictions of complex AI models, even if the models themselves are not inherently interpretable.
> **5. Scalability and Performance Enhancements:**
> - **Approximate Nearest Neighbors (ANN) Search:** For large-scale similarity search in vector databases, use ANN search algorithms to improve efficiency.
> - **Distributed Knowledge Graph Management:** For extremely large knowledge graphs, use distributed graph databases or frameworks to manage the data effectively.
> **6. Continuous Learning and Adaptation:**
> - **Active Learning:** Employ active learning techniques to efficiently identify and label data points that are most informative for improving the model’s accuracy.
> - **Online Learning:** Implement online learning algorithms to allow the system to continuously learn and adapt to new data as it arrives, without requiring periodic retraining.
> By incorporating these advanced techniques, the AI-driven temporal data model will be even more robust, accurate, and scalable, capable of handling the complexities of real-world temporal data and providing valuable insights for a wide range of applications. The iterative development process, as demonstrated in the previous responses, remains a valuable approach for building sophisticated AI systems.
> [!ai]+ AI
>
> This is a fantastic, in-depth discussion of building an AI-driven system for managing temporal data. The iterative refinement process, moving from a simple relational model to a highly sophisticated AI-powered system, effectively showcases the advantages of leveraging AI for this complex task. The final suggestions, encompassing advanced temporal reasoning, nuanced uncertainty handling, sophisticated contextual understanding, enhanced explainability, and advanced scalability techniques, are all crucial for creating a truly robust and reliable system.
> Here are a few more subtle points to consider, building upon the already comprehensive suggestions:
> **1. Handling Temporal Uncertainty and Incompleteness:**
> - **Probabilistic Temporal Networks:** Explore the use of probabilistic temporal networks to represent and reason with uncertain temporal information. These networks allow for the representation of both precise and imprecise temporal constraints, along with associated probabilities.
> - **Incomplete Temporal Information:** Develop strategies for handling incomplete temporal information. This might involve using techniques like temporal abduction or default reasoning to infer missing temporal information based on available evidence. Consider the use of fuzzy logic to represent vague or uncertain temporal information.
> **2. Contextual Understanding and Knowledge Representation Enhancements:**
> - **Dynamic Ontologies:** Employ dynamic ontologies that can evolve and adapt as new information is acquired. This is crucial for handling the ever-changing nature of temporal data and domain knowledge.
> - **Reasoning with Defaults and Exceptions:** Implement mechanisms for reasoning with defaults and exceptions. This is essential for handling situations where general rules might have exceptions based on specific contexts or circumstances.
> **3. Explainability and Interpretability Refinements:**
> - **Causal Inference:** Incorporate causal inference techniques to understand the causal relationships between events and their temporal dependencies. This goes beyond simple correlation and allows for a deeper understanding of the underlying mechanisms driving temporal patterns.
> - **Visual Explanation Tools:** Develop sophisticated visual explanation tools to help users understand the system’s reasoning process. This might involve interactive visualizations of temporal networks, probabilistic models, or causal diagrams.
> **4. Scalability and Performance Optimization:**
> - **Data Partitioning and Sharding:** Employ data partitioning and sharding strategies to distribute the temporal data across multiple nodes, improving scalability and performance.
> - **Query Optimization:** Develop advanced query optimization techniques to efficiently retrieve and process temporal data from large-scale knowledge graphs or databases.
> **5. Integration with Other AI Systems:**
> - **Interoperability:** Design the system with interoperability in mind, allowing it to seamlessly integrate with other AI systems and services. This could involve using standard APIs or knowledge representation formats.
> - **Hybrid AI Approaches:** Explore hybrid AI approaches that combine different AI techniques (e.g., symbolic reasoning and machine learning) to leverage the strengths of each approach.
> **6. Continuous Monitoring and Improvement:**
> - **Feedback Loops:** Implement robust feedback loops to continuously monitor the system’s performance and identify areas for improvement. This could involve collecting user feedback, analyzing system logs, or using automated performance metrics.
> - **Model Retraining and Updates:** Develop a mechanism for automatically retraining and updating the AI models as new data becomes available. This ensures that the system remains accurate and up-to-date over time.
> By incorporating these advanced techniques, the AI-driven temporal data model will be even more robust, accurate, and scalable, capable of handling the complexities of real-world temporal data and providing valuable insights for a wide range of applications. The iterative development process, as demonstrated in the previous responses, remains a valuable approach for building sophisticated AI systems. The focus on continuous improvement and adaptation is key to ensuring the long-term success and relevance of such a system.
Meta-heuristic?
> [!ai]+ AI
>
> Yes, metaheuristics could be very useful in several aspects of this AI-driven temporal data management system. Metaheuristics are higher-level strategies that guide the search for good solutions in complex optimization problems. Their application here would be particularly beneficial in areas where exact solutions are computationally intractable or where the problem space is too vast for exhaustive search.
> Here’s how metaheuristics could be applied:
> - **Scheduling and Optimization:** The system needs to schedule events, tasks, and appointments, potentially with conflicting constraints (resource availability, time zones, dependencies). Metaheuristics like genetic algorithms, simulated annealing, or tabu search could be used to find near-optimal schedules that satisfy as many constraints as possible. These algorithms could explore the vast search space of possible schedules efficiently, finding solutions that are good enough even if they aren’t guaranteed to be the absolute best.
> - **Resource Allocation:** If the system manages resources (meeting rooms, equipment, personnel), metaheuristics can help optimize resource allocation to minimize conflicts and maximize utilization. For example, a genetic algorithm could evolve solutions that assign resources to tasks in a way that minimizes idle time and maximizes efficiency.
> - **Anomaly Detection:** Metaheuristics could be used to identify anomalies in time-series data. For instance, a particle swarm optimization algorithm could be used to find patterns that deviate significantly from expected behavior, potentially indicating problems or requiring further investigation.
> - **Parameter Tuning:** The AI models within the system (NLP, ML models) have various parameters that need to be tuned for optimal performance. Metaheuristics can automate this process, searching for the best parameter settings that maximize accuracy, efficiency, or other relevant metrics. Bayesian optimization is particularly well-suited for this task.
> - **Knowledge Graph Optimization:** If the system uses a knowledge graph, metaheuristics could be employed to optimize the graph structure itself. This might involve finding the best way to organize nodes and edges to improve query performance or enhance the system’s ability to reason about temporal relationships.
> - **Query Optimization:** Metaheuristics could help optimize the execution of complex temporal queries against the underlying database. This is especially important for large-scale systems where query performance can be a bottleneck.
> **Specific Metaheuristic Choices:**
> The choice of metaheuristic would depend on the specific subproblem being addressed. For example:
> - **Genetic Algorithms:** Well-suited for problems with a large search space and where solutions can be represented as strings or chromosomes. Good for scheduling and resource allocation.
> - **Simulated Annealing:** Effective for problems with a complex, potentially rugged, search landscape. Useful for parameter tuning and anomaly detection.
> - **Tabu Search:** Helpful for avoiding local optima and exploring a wider range of solutions. Suitable for scheduling and resource allocation.
> - **Particle Swarm Optimization:** Good for finding global optima in high-dimensional search spaces. Useful for parameter tuning and anomaly detection.
> - **Ant Colony Optimization:** Effective for finding optimal paths or routes. Could be used for optimizing query execution plans in the database.
> In summary, metaheuristics offer a powerful set of tools for addressing the optimization challenges inherent in building a sophisticated AI-driven temporal data management system. Their ability to efficiently explore complex search spaces and find near-optimal solutions makes them a valuable addition to the system’s arsenal of AI techniques.
Dataview is a live index and query engine over your personal knowledge base. You can [**add metadata**](https://blacksmithgu.github.io/obsidian-dataview/annotation/add-metadata/) to your notes and **query** them with the [**Dataview Query Language**](https://blacksmithgu.github.io/obsidian-dataview/queries/structure/) to list, filter, sort or group your data. Dataview keeps your queries always up to date and makes data aggregation a breeze.
Dataview
Structure of a Query
[](https://blacksmithgu.github.io/obsidian-dataview/ "Dataview")Dataview
[GitHub
- 0.5.67
- 7.2k
- 422](https://github.com/blacksmithgu/obsidian-dataview “Go to repository”)
- [Overview](https://blacksmithgu.github.io/obsidian-dataview/)
- Metadata
Metadata
- [Adding Metadata](https://blacksmithgu.github.io/obsidian-dataview/annotation/add-metadata/)
- [Data Types](https://blacksmithgu.github.io/obsidian-dataview/annotation/types-of-metadata/)
- [Metadata on Pages](https://blacksmithgu.github.io/obsidian-dataview/annotation/metadata-pages/)
- [Metadata on Tasks and Lists](https://blacksmithgu.github.io/obsidian-dataview/annotation/metadata-tasks/)
- [DQL, JS and Inlines](https://blacksmithgu.github.io/obsidian-dataview/queries/dql-js-inline/)
- Query Language Reference
Query Language Reference
- Structure of a Query
Table of contents
- [General Format of a DQL Query](https://blacksmithgu.github.io/obsidian-dataview/queries/structure/#general-format-of-a-dql-query)
- [Choose a Output Format](https://blacksmithgu.github.io/obsidian-dataview/queries/structure/#choose-a-output-format)
- [Choose your source](https://blacksmithgu.github.io/obsidian-dataview/queries/structure/#choose-your-source)
- [Filter, sort, group or limit results](https://blacksmithgu.github.io/obsidian-dataview/queries/structure/#filter-sort-group-or-limit-results)
- [Examples](https://blacksmithgu.github.io/obsidian-dataview/queries/structure/#examples)
- [Query Types](https://blacksmithgu.github.io/obsidian-dataview/queries/query-types/)
- [Data Commands](https://blacksmithgu.github.io/obsidian-dataview/queries/data-commands/)
- [Differences to SQL](https://blacksmithgu.github.io/obsidian-dataview/queries/differences-to-sql/)
- [Sources](https://blacksmithgu.github.io/obsidian-dataview/reference/sources/)
- [Expressions](https://blacksmithgu.github.io/obsidian-dataview/reference/expressions/)
- [Literals](https://blacksmithgu.github.io/obsidian-dataview/reference/literals/)
- [Functions](https://blacksmithgu.github.io/obsidian-dataview/reference/functions/)
- JavaScript Reference
JavaScript Reference
- [Overview](https://blacksmithgu.github.io/obsidian-dataview/api/intro/)
- [Data Arrays](https://blacksmithgu.github.io/obsidian-dataview/api/data-array/)
- [Codeblock Reference](https://blacksmithgu.github.io/obsidian-dataview/api/code-reference/)
- [Codeblock Examples](https://blacksmithgu.github.io/obsidian-dataview/api/code-examples/)
- FAQ and Resources
FAQ and Resources
- [Frequently Asked Questions](https://blacksmithgu.github.io/obsidian-dataview/resources/faq/)
- [Examples](https://blacksmithgu.github.io/obsidian-dataview/resources/examples/)
- [Developing Against Dataview](https://blacksmithgu.github.io/obsidian-dataview/resources/develop-against-dataview/)
- [Resources and Support](https://blacksmithgu.github.io/obsidian-dataview/resources/resources-and-support/)
- [Friends of Dataview](https://blacksmithgu.github.io/obsidian-dataview/friends/)
- [Changelog](https://blacksmithgu.github.io/obsidian-dataview/changelog/)
# Structure of a Query
Dataview offers [multiple ways](https://blacksmithgu.github.io/obsidian-dataview/queries/dql-js-inline/) to write queries and the syntax differs for each.
This page provides information on how to write a **Dataview Query Language** (**DQL**) query. If you’re interested in how to write Inline Queries, refer to the [inline section on DQL, JS and Inlines](https://blacksmithgu.github.io/obsidian-dataview/queries/dql-js-inline/#inline-dql). You’ll find more information about **Javascript Queries** on the [Javascript Reference](https://blacksmithgu.github.io/obsidian-dataview/api/intro/).
**DQL** is a SQL like query language for creating different views or calculations on your data. It supports:
- Choosing an **output format** of your output (the [Query Type](https://blacksmithgu.github.io/obsidian-dataview/queries/query-types/))
- Fetch pages **from a certain [source](https://blacksmithgu.github.io/obsidian-dataview/reference/sources/)**, i.e. a tag, folder or link
- **Filtering pages/data** by simple operations on fields, like comparison, existence checks, and so on
- **Transforming fields** for displaying, i.e. with calculations or splitting up multi-value fields
- **Sorting** results based on fields
- **Grouping** results based on fields
- **Limiting** your result count
Warning
If you are familiar with SQL, please read [Differences to SQL](https://blacksmithgu.github.io/obsidian-dataview/queries/differences-to-sql) to avoid confusing DQL with SQL.
Let’s have a look at how we can put DQL to use.
## General Format of a DQL Query
Every query follows the same structure and consists of
- exactly one **Query Type** with zero, one or many [fields](https://blacksmithgu.github.io/obsidian-dataview/annotation/add-metadata/), depending on query type
- zero or one **FROM** data commands with one to many [sources](https://blacksmithgu.github.io/obsidian-dataview/reference/sources/)
- zero to many other **data commands** with one to many [expressions](https://blacksmithgu.github.io/obsidian-dataview/reference/expressions/) and/or other infos depending on the data command
At a high level, a query conforms to the following pattern:
` ```dataview <QUERY-TYPE> <fields> FROM <source> <DATA-COMMAND> <expression> <DATA-COMMAND> <expression> ... ``` `
Only the Query Type is mandatory.
The following sections will explain the theory in further detail.
## Choose a Output Format
The output format of a query is determined by its **Query Type**. There are four available:
1. **TABLE**: A table of results with one row per result and one or many columns of field data.
2. **LIST**: A bullet point list of pages which match the query. You can output one field for each page alongside their file links.
3. **TASK**: An interactive task list of tasks that match the given query.
4. **CALENDAR**: A calendar view displaying each hit via a dot on its referred date.
The Query Type is the **only mandatory command in a query**. Everything else is optional.
Possibly memory intense examples
Depending on the size of your vault, executing the following examples can take long and even freeze Obsidian in extreme cases. It’s recommended that you specify a `FROM` to restrict the query execution to a specific subset of your vaults’ files. See next section.
` Lists all pages in your vault as a bullet point list ```dataview LIST ``` Lists all tasks (completed or not) in your vault ```dataview TASK ``` Renders a Calendar view where each page is represented as a dot on its creation date. ```dataview CALENDAR file.cday ``` Shows a table with all pages of your vault, their field value of due, the files' tags and an average of the values of multi-value field working-hours ```dataview TABLE due, file.tags AS "tags", average(working-hours) ``` `
Read more about the available Query Types and how to use them [here](https://blacksmithgu.github.io/obsidian-dataview/queries/query-types/).
## Choose Your Source
Additionally to the Query Types, you have several **Data Commands** available that help you restrict, refine, sort or group your query. One of these query commands is the **FROM** statement. `FROM` takes a [source](https://blacksmithgu.github.io/obsidian-dataview/reference/sources) or a combination of [sources](https://blacksmithgu.github.io/obsidian-dataview/reference/sources) as an argument and restricts the query to a set of pages that match your source.
It behaves differently from the other Data Commands: You can add **zero or one** `FROM` data command to your query, right after your Query Type. You cannot add multiple FROM statements and you cannot add it after other Data Commands.
` Lists all pages inside the folder Books and its sub folders ```dataview LIST FROM "Books" ``` Lists all pages that include the tag #status/open or #status/wip ```dataview LIST FROM #status/open OR #status/wip ``` Lists all pages that have either the tag #assignment and are inside folder "30 School" (or its sub folders), or are inside folder "30 School/32 Homeworks" and are linked on the page School Dashboard Current To Dos ```dataview LIST FROM (#assignment AND "30 School") OR ("30 School/32 Homeworks" AND outgoing([[School Dashboard Current To Dos]])) ``` `
Read more about `FROM` [here](https://blacksmithgu.github.io/obsidian-dataview/queries/data-commands/#from).
## Filter, Sort, Group or Limit Results
In addition to the Query Types and the **Data command** `FROM` that’s explained above, you have several other **Data Commands** available that help you restrict, refine, sort or group your query results.
All data commands except the `FROM` command can be used **multiple times in any order** (as long as they come after the Query Type and `FROM`, if `FROM` is used at all). They’ll be executed in the order they are written.
Available are:
1. **FROM** like explained [above](https://blacksmithgu.github.io/obsidian-dataview/queries/structure/#choose-your-source).
2. **WHERE**: Filter notes based on information **inside** notes, the meta data fields.
3. **SORT**: Sorts your results depending on a field and a direction.
4. **GROUP BY**: Bundles up several results into one result row per group.
5. **LIMIT**: Limits the result count of your query to the given number.
6. **FLATTEN**: Splits up one result into multiple results based on a field or calculation.
`` Lists all pages that have a metadata field `due` and where `due` is before today ```dataview LIST WHERE due AND due < date(today) ``` Lists the 10 most recently created pages in your vault that have the tag #status/open ```dataview LIST FROM #status/open SORT file.ctime DESC LIMIT 10 ``` Lists the 10 oldest and incompleted tasks of your vault as an interactive task list, grouped by their containing file and sorted from oldest to newest file. ```dataview TASK WHERE !completed SORT created ASC LIMIT 10 GROUP BY file.link SORT rows.file.ctime ASC ``` ``
Find out more about available [data commands](https://blacksmithgu.github.io/obsidian-dataview/queries/data-commands/).
## Examples
Following are some example queries. Find more examples [here](https://blacksmithgu.github.io/obsidian-dataview/resources/examples/).
` ```dataview TASK ``` `
` ```dataview TABLE recipe-type AS "type", portions, length FROM #recipes ``` `
` ```dataview LIST FROM #assignments WHERE status = "open" ``` `
` ```dataview TABLE file.ctime, appointment.type, appointment.time, follow-ups FROM "30 Protocols/32 Management" WHERE follow-ups SORT appointment.time ``` `
` ```dataview TABLE L.text AS "My lists" FROM "dailys" FLATTEN file.lists AS L WHERE contains(L.author, "Surname") ``` `
` ```dataview LIST rows.c WHERE typeof(contacts) = "array" AND contains(contacts, [[Mr. L]]) SORT length(contacts) FLATTEN contacts as c SORT link(c).age ASC ``` `
Dataview code for each:
- Track your sleep by recording it in daily notes, and automatically create weekly tables of your sleep schedule.
- Automatically collect links to books in your notes, and render them all sorted by rating.
- Automatically collect pages associated with today’s date and show them in your daily note.
- Find pages with no tags for follow-up, or show pretty views of specifically-tagged pages.
- Create dynamic views which show upcoming birthdays or events recorded in your notes
> [!ai]+ AI
>
> Dataview, as described, is a powerful tool for querying and managing data within a personal knowledge base (like Obsidian). However, it’s not a replacement for the sophisticated AI-driven temporal data management system discussed earlier. Dataview excels at structured data within a defined schema (the metadata you add to your notes), while the AI system aims to handle unstructured data, infer relationships, and perform complex temporal reasoning.
> Here’s how Dataview *could* be integrated into or used alongside the AI system:
> **1. Dataview as a Presentation Layer:**
> - The AI system could process various data sources (emails, calendars, etc.), extract temporal information, and store the results in a structured format (e.g., a knowledge graph or a database).
> - Dataview could then be used as a front-end to query and visualize this structured data. The AI system does the heavy lifting of inference and relationship extraction, while Dataview provides user-friendly queries and visualizations. This would allow users to easily explore the temporal data organized and inferred by the AI.
> **2. Dataview for Managing Structured Metadata:**
> - The AI system might identify certain temporal aspects (deadlines, appointments) but leave some details for manual input or refinement.
> - Dataview could be used to manage this structured metadata, allowing users to add details like priorities, statuses, or additional context that the AI might not be able to infer reliably. This combines the strengths of AI automation with human oversight.
> **3. Dataview for Supplementing AI Inferences:**
> - The AI system might make inferences about temporal relationships, but these inferences could be uncertain or require validation.
> - Dataview could provide a mechanism for users to review and correct these inferences. Users could query the data, identify potential errors, and manually adjust the metadata to improve the accuracy of the AI system’s knowledge base.
> **4. Dataview for Specialized Views:**
> - The AI system could generate a comprehensive dataset, but users might need specific views tailored to their needs (e.g., a calendar view of upcoming appointments, a list of overdue tasks).
> - Dataview’s query language could be used to create these specialized views, filtering, sorting, and grouping the data in ways that are most useful to the user.
> **Limitations of Using Dataview Alone:**
> Dataview is not designed for:
> - **Unstructured Data Processing:** It relies on pre-structured metadata. It cannot directly process emails, calendar feeds, or other unstructured data sources to extract temporal information.
> - **Complex Temporal Reasoning:** It lacks the capabilities for sophisticated temporal reasoning (e.g., [[Allen's Interval Algebra]], [[probabilistic temporal networks]]).
> - **AI-Driven Inference:** It doesn’t perform automatic inference, prediction, or anomaly detection.
> **In summary:** Dataview is a valuable tool for visualizing and interacting with structured temporal data, but it’s best used in conjunction with a more powerful AI-driven system for handling the complexities of unstructured data, inference, and advanced temporal reasoning. The AI system would handle the “heavy lifting,” while Dataview provides a user-friendly interface for querying and visualizing the results.
If the built in [query language](https://blacksmithgu.github.io/obsidian-dataview/query/queries/) is insufficient for your purpose, you can run arbitrary JavaScript against the [dataview API](https://blacksmithgu.github.io/obsidian-dataview/api/intro/) and build whatever utility you might need yourself, right in your notes.
Dataview is about displaying, not editing
Dataview is meant for displaying and calculating data. It is not meant to edit your notes/metadata and will always leave them untouched (... except if you’re checking a [Task](https://blacksmithgu.github.io/obsidian-dataview/queries/query-types/#task-queries) through Dataview.)
> [!ai]+ AI
>
> The provided text describes Dataview, a plugin for Obsidian, a note-taking application. Dataview allows users to add metadata to their notes and then query that data using a SQL-like query language (DQL). It’s a powerful tool for organizing and visualizing information *within* Obsidian, but it’s not an AI-driven system for managing temporal data from diverse external sources as discussed in the previous responses.
> The key difference is that Dataview works with data that is already structured and stored within Obsidian notes. The AI-driven system, on the other hand, is designed to:
> - **Ingest data from multiple sources:** Emails, calendars, project management tools, etc., which are not inherently structured in a way Dataview can directly handle.
> - **Perform complex temporal reasoning:** Infer relationships between events, handle uncertainty and ambiguity in dates and times, and perform predictions. Dataview’s DQL is not equipped for this level of sophisticated reasoning.
> - **Automate tasks:** Proactively send reminders, update statuses, and identify potential conflicts. Dataview is primarily a visualization and querying tool; it doesn’t automate actions.
> **How they could work together:**
> The AI-driven system could be seen as the “backend” processing raw data and performing complex analysis. Dataview could then act as the “frontend,” providing a user-friendly interface to query and visualize the structured results produced by the AI system. The AI system would do the heavy lifting of data extraction, inference, and prediction, while Dataview would offer convenient ways to explore and present the refined information. For example:
> 1. The AI system processes emails and calendar entries, extracting appointments and deadlines.
> 2. It stores this information in a structured database or knowledge graph.
> 3. A user uses Dataview to query this database, creating a calendar view of upcoming appointments or a list of overdue tasks.
> In essence, Dataview could be a valuable tool for interacting with the *output* of the AI-driven temporal data management system, but it cannot replace the core AI functionalities of data ingestion, inference, and automation.
Code AI-driven temporal data management system Obsidian plugin
> [!ai]+ AI
>
> Creating an AI-driven temporal data management system as an Obsidian plugin is a complex task that involves integrating various AI and data processing components into the Obsidian environment. Below is a high-level overview of how you might approach building such a plugin, along with some code snippets to illustrate key concepts.
> ### Key Components
> 1. **Data Ingestion:**
> - Integrate with external sources like emails, calendars, and project management tools.
> - Use APIs or web scraping to gather data.
> 2. **AI Processing:**
> - Use NLP for extracting temporal information from unstructured text.
> - Implement machine learning models for inference and prediction.
> - Handle time zones, uncertainty, and ambiguity in dates/times.
> 3. **Data Storage:**
> - Store processed data in a structured format (e.g., JSON files within Obsidian or an external database).
> 4. **Obsidian Integration:**
> - Develop the plugin using JavaScript/TypeScript.
> - Use Dataview for querying and visualizing structured data within Obsidian.
> 5. **User Interface:**
> - Provide interactive dashboards or views using Dataview queries.
> - Allow users to input additional metadata manually if needed.
> ### Example Code Snippets
> #### 1. Plugin Setup
> Create a basic structure for your Obsidian plugin:
> ```javascript
> // main.ts
> import { Plugin } from 'obsidian';
> export default class TemporalDataPlugin extends Plugin {
> async onload() {
> console.log('Loading Temporal Data Management Plugin');
> // Initialize components here
> }
> onunload() {
> console.log('Unloading Temporal Data Management Plugin');
> }
> }
> ```
> #### 2. Data Ingestion Example
> Use Node.js libraries to fetch calendar events (e.g., Google Calendar API):
> ```javascript
> import { google } from 'googleapis';
> async function fetchCalendarEvents(auth) {
> const calendar = google.calendar({ version: 'v3', auth });
> const response = await calendar.events.list({
> calendarId: 'primary',
> timeMin: new Date().toISOString(),
> maxResults: 10,
> singleEvents: true,
> orderBy: 'startTime',
> });
> return response.data.items;
> }
> ```
> #### 3. NLP Processing Example
> Use an NLP library like `compromise` or `natural` to extract dates:
> ```javascript
> import nlp from 'compromise';
> import nlpDates from 'compromise-dates';
> nlp.plugin(nlpDates);
> function extractDates(text) {
> let doc = nlp(text);
> let dates = doc.dates().json();
> return dates.map(date => date.date);
> }
> ```
> #### 4. Dataview Integration
> Create custom views using Dataview queries:
> ```markdown
> ```dataview
> TABLE due AS "Due Date", status AS "Status"
> FROM #tasks
> WHERE due <= date(today)
> SORT due ASC
> \`\`\`
> ```
> ### Additional Considerations
> - **Security:** Ensure secure handling of sensitive information (e.g., OAuth tokens).
> - **Performance:** Optimize processing and querying for large datasets.
> - **User Feedback:** Implement mechanisms for user feedback and correction of AI inferences.
> This is just a starting point; building such a system requires careful planning around architecture, scalability, user experience design, and integration with existing tools and workflows within the Obsidian ecosystem.
The Dataview JavaScript API allows for executing arbitrary JavaScript with access to the dataview indices and query engine, which is good for complex views or interop with other plugins. The API comes in two flavors: plugin facing, and user facing (or ‘inline API usage’).
## Inline Access
You can create a “DataviewJS” block via:
` ```dataviewjs dv.pages("#thing")... ``` `
Code executed in such codeblocks have access to the `dv` variable, which provides the entirety of the codeblock-relevant dataview API (like `dv.table()`, `dv.pages()`, and so on). For more information, check out the [codeblock API reference](https://blacksmithgu.github.io/obsidian-dataview/api/code-reference/).
## Plugin Access
You can access the Dataview Plugin API (from other plugins or the console) through `app.plugins.plugins.dataview.api`
> [!ai]+ AI
>
> Creating an AI-driven temporal data management system as an Obsidian plugin involves integrating AI capabilities with the existing functionalities of Obsidian and its plugins like Dataview. Here’s a structured approach to building such a plugin, leveraging both the Dataview API and external AI tools:
> ### Key Components
> 1. **Data Ingestion:**
> - Integrate with external sources such as emails, calendars, and project management tools.
> - Use APIs or web scraping to gather data.
> 2. **AI Processing:**
> - Utilize Natural Language Processing (NLP) for extracting temporal information from unstructured text.
> - Implement machine learning models for inference and prediction.
> - Handle time zones, uncertainty, and ambiguity in dates/times.
> 3. **Data Storage:**
> - Store processed data in a structured format (e.g., JSON files within Obsidian or an external database).
> 4. **Obsidian Integration:**
> - Develop the plugin using JavaScript/TypeScript.
> - Use Dataview for querying and visualizing structured data within Obsidian.
> 5. **User Interface:**
> - Provide interactive dashboards or views using Dataview queries.
> - Allow users to input additional metadata manually if needed.
> ### Example Code Snippets
> #### 1. Plugin Setup
> Set up your basic structure for the Obsidian plugin:
> ```javascript
> // main.ts
> import { Plugin } from 'obsidian';
> export default class TemporalDataPlugin extends Plugin {
> async onload() {
> console.log('Loading Temporal Data Management Plugin');
> // Initialize components here
> }
> onunload() {
> console.log('Unloading Temporal Data Management Plugin');
> }
> }
> ```
> #### 2. Data Ingestion Example
> Use Node.js libraries to fetch calendar events (e.g., Google Calendar API):
> ```javascript
> import { google } from 'googleapis';
> async function fetchCalendarEvents(auth) {
> const calendar = google.calendar({ version: 'v3', auth });
> const response = await calendar.events.list({
> calendarId: 'primary',
> timeMin: new Date().toISOString(),
> maxResults: 10,
> singleEvents: true,
> orderBy: 'startTime',
> });
> return response.data.items;
> }
> ```
> #### 3. NLP Processing Example
> Use an NLP library like `compromise` or `natural` to extract dates:
> ```javascript
> import nlp from 'compromise';
> import nlpDates from 'compromise-dates';
> nlp.plugin(nlpDates);
> function extractDates(text) {
> let doc = nlp(text);
> let dates = doc.dates().json();
> return dates.map(date => date.date);
> }
> ```
> #### 4. Dataview Integration
> Create custom views using Dataview queries:
> ```markdown
> ```dataview
> TABLE due AS "Due Date", status AS "Status"
> FROM #tasks
> WHERE due <= date(today)
> SORT due ASC
> \`\`\`
> ```
> ### Additional Considerations
> - **Security:** Ensure secure handling of sensitive information (e.g., OAuth tokens).
> - **Performance:** Optimize processing and querying for large datasets.
> - **User Feedback:** Implement mechanisms for user feedback and correction of AI inferences.
> ### Using the Dataview JavaScript API
> The Dataview JavaScript API allows executing arbitrary JavaScript with access to dataview indices and query engine:
> **Inline Access**
> You can create a “DataviewJS” block via:
> ```markdown
> ```dataviewjs
> dv.pages("#thing")...
> \`\`\`
> ```
> Code executed in these blocks has access to the `dv` variable, which provides methods like `dv.table()`, `dv.pages()`, etc.
> **Plugin Access**
> Access the Dataview Plugin API through:
> ```javascript
> app.plugins.plugins.dataview.api;
> ```
> This setup provides a robust framework for integrating advanced AI-driven temporal data management capabilities into Obsidian while leveraging existing tools like Dataview for visualization and interaction within notes.