286 Matching Annotations
  1. Dec 2020

      gambar model yang sederhana. akan bagus kalau kita dapat menggambarkan sendiri (walaupun hanya dengan tangan) interaksi yang sama di daerah kita.

    1. Eyal describes the theory called The Fogg Behavior Model which states that for a behavior (B) to occur, three things must be present at the same time: motivation (M), ability (A), and a trigger (T). More succinctly, B = MAT.

      Fogg Behavior Model says that for a Behavior (B) to occur 3 things have to be present at the same time:

      1. Motivation (M)
      2. Ability (A)
      3. Trigger (T)

      B = MAT

    1. When it comes to online marketplace models, your monetization strategies prevail over all odds. Your online marketplace model must be developed keeping the money-making technique in mind while the funding-flow should majorly be directed from the community you actually serve.

      When it comes to the monetization model for online marketplace, you can find plenty of ways to earn money. However, you need to ensure what types of marketplace you want to build, and which monetization strategies will fit into it.

    1. Better community building: At the moment, MDN content edits are published instantly, and then reverted if they are not suitable. This is really bad for community relations. With a PR model, we can review edits and provide feedback, actually having conversations with contributors, building relationships with them, and helping them learn.
    2. Better contribution workflow: We will be using GitHub’s contribution tools and features, essentially moving MDN from a Wiki model to a pull request (PR) model. This is so much better for contribution, allowing for intelligent linting, mass edits, and inclusion of MDN docs in whatever workflows you want to add it to (you can edit MDN source files directly in your favorite code editor).
    1. Airbnb helps travellers home-like travelling experiences by helping you rent a property. Initially, the idea was criticized and did not receive much hype as letting unknown people stay home was not a good idea at all. However, Airbnb successfully changed the dynamics of renting homes. That means the credit goes to the business model that was convincing enough for guests and hosts.

      Airbnb is a community based online marketplace, offering customers to make an arrangement for their lodging, primarily homestays, or tourism experiences.

  2. Nov 2020
    1. Linear mixed models are an extension of simple linear models to allow both fixed and random effects, and are particularly used when there is non independence in the data, such as arises from a hierarchical structure
    1. We love dbt because of the values it embodies. Individual transformations are SQL SELECT statements, without side effects. Transformations are explicitly connected into a graph. And support for testing is first-class. dbt is hugely enabling for an important class of users, adapting software engineering principles to a slightly different domain with great ergonomics. For users who already speak SQL, dbt’s tooling is unparalleled.

      when using [[dbt]] the [[transformations]] are [[SQL statements]] - already something that our team knows

    1. We then estimate the relative weight each touch played in leading to a conversion. This estimation is done by allocating “points” to touches: each conversion is worth exactly one point, and that point is divvied up between the customer’s touches. There are four main ways to divvy up this point:First touch: Attribute the entire conversion to the first touchLast touch: Attribute the entire conversion to the last touchForty-twenty-forty: Attribute 40% (0.4 points) of the attribution to the first touch, 40% to the last touch, and divide the remaining 20% between all touches in betweenLinear: Divide the point equally among all touches

      [[positional attribution]] works by identifying the touch points in the lifecycle, and dividing up the points across those touches.

      There are four main ways to divvy up this pointing

      [[question]] What are the four main ways to divvy up positional attribution]]

      • [[first touch]]
      • [[last touch]]
      • [[fourty-twenty-fourty]]
      • [[linear]]
    2. Once you have pageviews in your warehouse, you’ll need to do two thingsSessionization: Aggregate these pageviews into sessions (or “sessionization”) writing logic to identify gaps of 30 minutes or more.User stitching: If a user first visits your site without any identifying information (typically a `customer_id` or `email`), and then converts at a later date, their previous (anonymous) sessions should be updated to include their information. Your web tracking system should have a way to link these sessions together.This modeling is pretty complex, especially for companies with thousands of pageviews a day (thank goodness for incremental models 🙌). Fortunately, some very smart coworkers have written packages to do the heavy lifting for you, whether your page views are tracked with Snowplow, Segment or Heap. Leverage their work by installing the right package to transform the data for you.

      [[1. Gather your required data sources]] - once we have data, we need to do two things [[sessionization]] - the aggregation of pageviews / etc into a session

      and [[user stitching]] - when we have a user without any identifying information, and then converts - kind of like the anonymous users / signups - and trying to tie them back to a source

    3. 1. Gather your required data sourcesSessions:Required dbt techniques: packagesWe want to use a table that represents every time a customer interacts with our brand. For ecommerce companies, the closest thing we can get to for this is sessions. (If you’re instead working for a B2B organization, you should consider using a table of interactions between your sales team and a potential customer from your CRM).Sessions are discrete periods of activity by a user on a website. The industry standard is to define a session as a series of activities followed by a 30-minute window without any activity.

      [[1. Gather your required data sources]]

    4. How to build an attribution model

      [[How to build an attribution model]]

      • [[1. Gather your required data sources]]
      • [[2. Find all sessions before conversion]]
      • [[3. Calculate the total sessions and the session index]]
      • [[3. Allocate points]]
      • [[4. Bonus Join in revenue value]]
      • [[5. Bonus Join with ad spend data]]
      • [[6. Ship it!]]
    5. The attribution data modelIn reality, it’s impossible to know exactly why someone converted to being a customer. The best thing that we can do as analysts, is provide a pretty good guess. In order to do that, we’re going to use an approach called positional attribution. This means, essentially, that we’re going to weight the importance of various touches (customer interactions with a brand) based on their position (the order they occur in within the customer’s lifetime).To do this, we’re going to build a table that represents every “touch” that someone had before becoming a customer, and the channel that led to that touch.

      One of the goals of an [[attribution data model]] is to understand why someone [[converted]] to being a customer. This is impossible to do accurately, but this is where analysis comes in.

      There are some [[approaches to attribution]], one of those is [[positional attribution]]

      [[positional attribution]] is that we are weighting the importance of touch points - or customer interactions, based on their position within the customer lifetime.

    6. transparent attribution model. You’re not relying on vendor logic. If your sales team feels like your attribution is off, show them dbt docs, walk them through the logic of your model, and make modifications with a single line of SQL

      [[transparent attribution model]]

    7. The most flexible attribution model. You own the business logic and you can extend it however you want, and change it easily when you business changes

      [[flexible attribution model]]

    8. hat’s it. Really! By writing SQL on top of raw data you get: The cheapest attribution model. This playbook assumes you’re operating within a modern data stack , so you already have the infrastructure that you need in place: You’re collecting events data with a tool like Snowplow or Segment (though Segment might get a little pricey) You’re extracting data from ad platforms using Stitch or Fivetran You’re loading data into a modern, cloud data warehouse like Snowflake, BigQuery, or Redshift And you’re using dbt so your analysts can model data in SQL

      [[cheapest attribution model]]

    9. So what do you actually need to build an attribution model?Raw data in your warehouse that represents customer interactions with your brand. For ecommerce companies, this is website visits. For B2B customers, it might be conversations with sales teams.SQL

      to build an [[attribution model]] we need the raw data - this raw data should capture the [[customer interactions]], and in our case - also partner interactions, or people working with the partner?

    1. This is addressing a security issue; and the associated threat model is "as an attacker, I know that you are going to do FROM ubuntu and then RUN apt-get update in your build, so I'm going to trick you into pulling an image that ​_pretents_​ to be the result of ubuntu + apt-get update so that next time you build, you will end up using my fake image as a cache, instead of the legit one." With that in mind, we can start thinking about an alternate solution that doesn't compromise security.
    1. Some people believe that online marketplace and aggregator business models work on the same principles. However, there is a fine line between these two. Let’s first clear out those and then move forward to understand the business model of the online marketplace.

      In this article, we are going to dive deeper into the business model of marketplace businesses. But before that, let’s clear out some misconceptions around online marketplaces.

  3. Oct 2020
    1. In order to inform the development and implementation of effective online learning environments, this study was designed to explore both instructors' and students' online learning experiences while enrolled in various online courses. The study investigated what appeared to both support and hinder participants' online teaching and learning experiences.

      The authors discuss the issue of community and engagement in online graduate programs. They carried out a small case study and used a Cognitive Apprenticeship Model to examine a successful program in Higher Education. They found that students feel too many online classes are just reading and writing, regurgitating rather than applying, and lack sufficient connection with the instructor and with other students, They recommend some strategies to fix that, but admit that more work is needed. 9/10

    1. The educator’s role in self-directed learning

      Fostering self-directed learning through strategy is discussed by Bailey et al. (2019) in chapter 1 of “Self-Directed Learning for the 21st Century: Implications for Higher Education.” The authors review the changing role of the educator and the learner based on respective self-directed teaching strategies (problem-based learning, cooperative learning, process-oriented learning) and the learner’s propensity for self-directed learning. In addition to providing principles to promote self-directed learning, the Grow and Borich models for implementing said learning were briefly reviewed. 8/10

    1. Cognitive Presence “is the extent to which learners are able to construct and confirm meaning through sustained reflection and discourse” (Community of Inquiry, n.d, para. 5). Video is often used as a unidirectional medium with information flowing from the expert or instructor to the learner. To move from transmission of content to construction of knowledge, tools such as Voice Thread (VoiceThread, 2016) support asynchronous conversation in a multimedia format.

      The author, Kendra Grant, is the Director of Professional Development and Learning for Quillsoft in Toronto Canada. Grant helps business succeed in education design and support. In this article Grant discusses how quickly the learning environment has changed through technological development. Grant explores the RAT Model, which guides instructors in the "use of technology to help transform instructional practice." Grant then examines the Community of Inquiry model, which seeks to create meaningful instruction through social, cognitive and teaching presence. Grant concludes by providing general principles for creating a positive video presence.

      Rating: 8/10

    1. virtual-dom exposes a set of objects designed for representing DOM nodes. A "Document Object Model Model" might seem like a strange term, but it is exactly that. It's a native JavaScript tree structure that represents a native DOM node tree.
  4. Sep 2020
    1. BPMN Viewer and Editor Use bpmn-js to display BPMN 2.0 diagrams on your website. Embed it as a BPMN 2.0 web modeler into your applications and customize it to suit your needs.
    1. mongoose.model


      When you call mongoose.model() on a schema, Mongoose compiles a model for you. The first argument is the singular name of the collection your model is for. Mongoose automatically looks for the plural, lowercased version of your model name. https://mongoosejs.com/docs/models.html#compiling

    1. Larremore, D. B., Wilder, B., Lester, E., Shehata, S., Burke, J. M., Hay, J. A., Tambe, M., Mina, M. J., & Parker, R. (2020). Test sensitivity is secondary to frequency and turnaround time for COVID-19 surveillance. MedRxiv, 2020.06.22.20136309. https://doi.org/10.1101/2020.06.22.20136309

    1. Baker, C. M., Campbell, P. T., Chades, I., Dean, A. J., Hester, S. M., Holden, M. H., McCaw, J. M., McVernon, J., Moss, R., Shearer, F. M., & Possingham, H. P. (2020). From climate change to pandemics: Decision science can help scientists have impact. ArXiv:2007.13261 [Physics]. http://arxiv.org/abs/2007.13261

    1. Aleta, A., Martín-Corral, D., Pastore y Piontti, A., Ajelli, M., Litvinova, M., Chinazzi, M., Dean, N. E., Halloran, M. E., Longini Jr, I. M., Merler, S., Pentland, A., Vespignani, A., Moro, E., & Moreno, Y. (2020). Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behaviour, 1–8. https://doi.org/10.1038/s41562-020-0931-9

  5. Aug 2020
    1. Candido, D. S., Claro, I. M., Jesus, J. G. de, Souza, W. M., Moreira, F. R. R., Dellicour, S., Mellan, T. A., Plessis, L. du, Pereira, R. H. M., Sales, F. C. S., Manuli, E. R., Thézé, J., Almeida, L., Menezes, M. T., Voloch, C. M., Fumagalli, M. J., Coletti, T. M., Silva, C. A. M. da, Ramundo, M. S., … Faria, N. R. (2020). Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science. https://doi.org/10.1126/science.abd2161

    1. The RAT model sees software development as an off-line program-construction activity composed of these parts: defining, decomposing, estimating, implementing, assembling, and finishing

      This is what can lead to the 'there is only version 1.0' problem - and improvements / iterations fall to the sidelines.

      This can have a number of consequences

      • over designed / engineered
      • doing unnecessary work
      • lack of user feedback and ability to accommodate it
      • rigid / fragile architecture
    1. Kreye, J., Reincke, S. M., Kornau, H.-C., Sánchez-Sendin, E., Corman, V. M., Liu, H., Yuan, M., Wu, N. C., Zhu, X., Lee, C.-C. D., Trimpert, J., Höltje, M., Dietert, K., Stöffler, L., Wardenburg, N. von, Hoof, S. van, Homeyer, M. A., Hoffmann, J., Abdelgawad, A., … Prüss, H. (2020). A SARS-CoV-2 neutralizing antibody protects from lung pathology in a COVID-19 hamster model. BioRxiv, 2020.08.15.252320. https://doi.org/10.1101/2020.08.15.252320

    1. Malani, A., Soman, S., Asher, S., Novosad, P., Imbert, C., Tandel, V., Agarwal, A., Alomar, A., Sarker, A., Shah, D., Shen, D., Gruber, J., Sachdeva, S., Kaiser, D., & Bettencourt, L. M. A. (2020). Adaptive Control of COVID-19 Outbreaks in India: Local, Gradual, and Trigger-based Exit Paths from Lockdown (Working Paper No. 27532; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27532

  6. Jul 2020
    1. Fajgelbaum, P., Khandelwal, A., Kim, W., Mantovani, C., & Schaal, E. (2020). Optimal Lockdown in a Commuting Network (Working Paper No. 27441; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27441