2016 Google Food Trends Report
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
UC Riverside UCR Honors Capstones 2016-2017
UC Riverside UCR Honors Capstones 2016-2017 Title GTWENDS Permalink https://escholarship.org/uc/item/41g0w0q9 Author Asfour, Mark Publication Date 2017-12-08 Data Availability The data associated with this publication are within the manuscript. eScholarship.org Powered by the California Digital Library University of California GTWENDS By Mark Jeffrey Asfour A capstone project submitted for Graduation with University Honors October 20, 2016 University Honors University of California, Riverside APPROVED ______________________________ Dr. Evangelos Christidis Department of Computer Science & Engineering ______________________________ Dr. Richard Cardullo, Howard H Hays Chair and Faculty Director, University Honors Associate Vice Provost, Undergraduate Education Abstract GTWENDS is an online interactive map of the United States of America that displays the locations of trending Twitter tweets, Google Search trends, and Google Hot Trends topics. States on the map are overlaid with a blue color where Twitter trends originate and a red color where Google trends originate with respective opacity levels varying based on the levels of interest from each website. Through the use of web crawling, map-reducing, and utilizing distributed processing, this project allows visitors to have an interactive geographic visual representation of current national social media topic activities. Visitors can use GTWENDS to learn about social media trends by observing where trends originate from, comparing the contrasts and similarities between trends from Twitter and Google, understanding what types of events trigger mass social media sharing, and much more. ii Acknowledgements I have worked on and developed GTWENDS with my classmates, Mehran Ghamaty (University of California, Riverside Spring 2016) and Jacob Xu (University of California, Riverside Spring 2016), during our senior design project class, CS 179G Databases Spring 2016, under the supervision of my professor and mentor, Professor Evangelos Christidis. -
Finding the Face of Your Data
Finding the Face of Your Data There’s been an explosion in data assets Growth of the “digital universe”1 Data overload in context2 40,000 1 EB = 1 billion gigabytes x70 30,000 20,000 10,000 Exabytes 2009 2020 IDC estimates that “tagged” information accounts for only about 3% The amount of information generated by humanity during the first of the digital universe, with analyzed information at 0.5%. The value day of a baby’s life today is equivalent to 70 times the information of big data technology lies in exploring the “untapped pools.” contained in the Library of Congress. Enterprise expectations are as big as the data Big data spending forecast by component3 50 Compute 31% CAGR Storage 40 Networking 30 Infrastructure Software SQL Database Software 20 NoSQL Database Software Application Software 10 Professional Services Billion Dollars Xaas 2011 2012 2013 2014 2015 2016 2017 According to a Wikibon study, big data spend will shift from infrastructure and middleware to value-add services and software during the next five years. Infrastructure, middleware, and technical services will likely become increasingly commoditized as they mature and common standards are adopted. We also note that this study did not include the costs associated with the business and domain experts’ time – a critical element of actionable insight. Taking advantage of data requires new tools ... Traditional and non-traditional value in data Data diving Pattern finding In taking advantage of new data assets – The other side of that same coin, from internal, external, structured, and seeking and patterning for previously unstructured data – and analytics tools, unasked and unanswerable questions, the most common form of value is realized is less common, but potentially more through exploiting deeper detail for new important to the enterprise. -
Unit 4 Lesson 1
Unit 4 Lesson 1 What is Big Data? Resources Name(s)_______________________________________________ Period ______ Date ________________ Unit 4 Lesson 01 Activity Guide - Big Data Sleuth Card Directions: Web Sites: ● With a partner, select one of the tools in the list to the right. 1. Web archive http://www.archive.org ● Determine what the tool is showing. 2. Measure of America http://www.measureofamerica.org/maps/ ● Find the source of the data it allows you to explore. 3. Wind Sensor network http://earth.nullschool.net/ ● Complete the table below. 4. Twitter sentiment https://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/ 5. Alternative Fuel Locator http://www.afdc.energy.gov/locator/stations/ Website Name What is this website potentially useful for? What kinds of problems could the provided information be used to solve? Is the provided visualization useful? Does it provide insight into the data? How does it help you look at a lot of information at once? How could it improve? Where is the data coming from? Check for “About”, “Download”, or “API”. You may also need to do a web search. ● Is the data from one source or many? ● Is it static or live? ● Is the source reputable? Why or why not? ● Add a link to the raw data if you can find one. Do you consider this “big” data? Explain your reasoning. 1 Unit 4 Lesson 2 Finding Trends with Visualizations Resources Unit 4 Lesson 2 Name(s)_______________________________________________ Period ______ Date ___________________ Activity Guide - Exploring Trends What’s a Trend? When you post information to a social network, watch a video online, or simply search for information on a search engine, some of that data is collected, and you reveal what topics are currently on your mind. -
Scratchboard Kitchen
SCRATCHBOARD KITCHEN OPEN WED - SUN 8AM-3PM | PLEASE NOTIFY US OF ANY ALLERGIES OR DIETARY RESTRICTIONS NOTE: MENU IS SUBJECT TO CHANGE BASED ON AVAILABILITY OF PRODUCTS FROM LOCAL FARMS To Start On Bread Upgraded Classics PASTRY BOARD LOX ON TOAST SHORT RIB HASH A SEASONAL POPTART, SCONE & MUFFIN 14 LEMON CHIVE CREAM CHEESE, CAPERS, EGGS 15 (GF* +2) POTATOES, ONIONS, GIARDINAIRE SEASONING, EGG (GF*) 17 CITRUS TOAST DUTCH BABY & GRAVY MASCARPONE, CITRUS, FENNEL POLLEN, PISTACHIO 13 (GF* +2) DUTCH BABY PANCAKE, SAUSAGE GRAVY, EGG 12 *PLEASE ALLOW 15-20 MINUTES* EGGS ON EGGS Feel Good Fare PANCAKES SOFT SCRAMBLED EGGS, REGIIS OVA CAVIAR, CHALLAH 15 (GF* +2) BANANA NUT OATMEAL SPELT FLOUR, METRIC COFFEE SYRUP 8, 10, 15 BANANAS, DATES, PECANS 8 SINGLE DOUBLE TRIPLE APPLE & PROSCIUTTO GRILLED CHEESE *PLEASE ALLOW 15-20 MINUTES* BRUNCH SALAD DIJON, GRUYERE, SOURDOUGH, WITH TOMATO SOUP 14 (GF* +2) SPINACH, BEETS, BACON, MUSTARD VINAIGRETTE (GF) 15 BEETROOT WAFFLE ADD CHICKEN +5 / FRIED CHICKEN +6 / TOFU +3 PISTACHIO, CHERRY JAM, LEMON WHIP (GF*) 12 BREAKFAST BISCUIT SANDWICH GRAIN BOWL BACON, EGG, CHEDDAR, BUTTERMILK BISCUIT 9 (GF* +2) +IMPOSSIBLE SAUSAGE (V) +4 SEASONAL FARM-FRESH VEGGIES, GRAINS (VG, GF*) 13 ADD SEARED TOFU +3 FRIED CHICKEN SANDWICH Extras CRISPY POTATOES WITH SPICY MAYO (GF*) 5 PIMIENTO CHEESE, RANCH BISCUIT, PICKLED ONIONS 11 MAKE IT SPICY (+1) OR DANGEROUSLY SPICY (+3) BRULÉED GRAPEFRUIT WITH CAYENNE & LIME (GF*) 3 AVOCADO GRILLED, WITH ZA’ATAR (GF*) 5 BISCUIT WITH LOCAL HONEY BUTTER & JAM 5 (GF* +1) CREAMY GRITS WITH CHEDDAR -
Apache Hadoop & Spark – What Is It ?
Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Apache Hadoop & Spark { What is it ? Demo Joseph Allemandou JoalTech 17 / 05 / 2018 1 / 23 Hello! Hadoop { Spark Overview Joseph Allemandou J. Allemandou Generalities Hadoop Spark Demo 2 / 23 Plan Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark 1 Generalities on High Performance Computing (HPC) Demo 2 Apache Hadoop and Spark { A Glimpse 3 Demonstration 3 / 23 More computation power: Scale up vs. scale out Hadoop { Spark Overview J. Allemandou Scale Up Scale out Generalities Hadoop Spark Demo 4 / 23 More computation power: Scale up vs. scale out Hadoop { Spark Overview Scale out J. Allemandou Scale Up Generalities Hadoop Spark Demo 4 / 23 Parallel computing Hadoop { Spark Overview Things to consider when doing parallel computing: J. Allemandou Partitioning (tasks, data) Generalities Hadoop Spark Communications Demo Synchronization Data dependencies Load balancing Granularity IO Livermore Computing Center - Tutorial 5 / 23 Looking back - Since 1950 Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Ngram Viewer 6 / 23 Looking back - Since 1950 Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Ngram Viewer 6 / 23 Looking back - Since 1950 Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Ngram Viewer 6 / 23 Looking back - Recent times Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Trends 7 / 23 Same problem, different tools Hadoop { Spark Overview J. Allemandou Generalities Supercomputer Big Data Hadoop Spark Demo Dedicated hardware Commodity hardware Message Passing Interface 8 / 23 MPI Hadoop { Spark Overview J. Allemandou Generalities C / C++ / Fortran / Python Hadoop Spark Demo Low-level API - Send / receive messages a lot to do manually split the data assign tasks to workers handle synchronisation handle errors 9 / 23 Hadoop + Spark Hadoop { Spark Overview J. -
Latency: Certain Operations Have a Much Higher Latency Than Other Operations Due to Network Communication
Gi2M+v "B; .i MHvbBb rBi? a+H M/ aT`F >2i?2` JBHH2` Distribution Distribution introduces important concerns beyond what we had to worry about when dealing with parallelism in the shared memory case: 111-- Partial failure: crash failures of a subset of the machines involved in a distributed computation . ...,. Latency: certain operations have a much higher latency than other operations due to network communication. Distribution Distribution introduces important concerns beyond what we had to worry about when dealing with parallelism in the shared memory case: 111-- Partial failure: crash failures of a subset of the machines involved in a distributed computation . ...,. Latency: certain operations have a much higher latency than other operations due to network communication. Latency cannot be masked completely; it will be an important aspect that also impacts the programming model. Important Latency Numbers L 1 cache reference 0.5ns Branch mispredict 5ns L2 cache reference 7ns Mutex lock/unlock 25ns Main memory reference l00ns Compress lK bytes with Zippy 3,000ns == 3µs Send 2K bytes over lGbps network 20,000ns == 20µs SSD random read 150,000ns == 150µs Read 1 MB sequentially from 250,000ns == 250µs Roundtrip within same datacenter 500,000ns == 0.5ms Read 1MB sequentially from SSD 1,000,000ns == lms Disk seek 10,000,000ns == l0ms Read 1MB sequentially from disk 20,000,000ns == 20ms Send packet US ---+ Europe ---+ US 150,000,000ns == 150ms Original compilation by Jeff Dean & Peter Norvig, w/ contributions by Joe Hellerstein & Erik Meijer -
Unemployment Rate Forecasting Using Google Trends Bachelor Thesis in Econometrics & Operations Research
Unemployment rate forecasting using Google trends Bachelor Thesis in Econometrics & Operations Research erasmus university rotterdam erasmus school of economics Author: Olivier O. Smit, 415283 Supervisor: Jochem Oorschot July 8, 2018 Abstract In order to make sound economic decisions, it is of great importance to be able to predict and interpret macro-economic variables. Researchers are therefore seeking continuously to improve the prediction performance. One of the main economic indicators is the US unemployment rate. In this paper, we empirically analyze whether, and to what extent, Google search data have additional predictive power in forecasting the US unemployment rate. This research consists of two parts. First, we look for and select Google search data with potential predicitive power. Second, we evaluate the performance of level and directional forecasts. Here, we make use of different models, based on both econometric and machine learning techniques. We find that Google trends improve the predictive accuracy in all used forecasting methods. Lastly, we discuss the limitations of our research and possible future research suggestions. 1 1 Introduction Nowadays, search engines are intensely used platforms. They serve as the gates to the Internet and at the same time help users to create order in the immense amount of websites and data available on the Internet. About a decade ago, researchers have realized that these search engines contain an enormous quantity of new data that can be of great additional value for modeling all kinds of processes. Previous researches have already proven this to be true. For example, Constant and Zimmermann (2008) have shown that including Google - the largest search engine in terms of search activity and amount of users - query data can be very useful in measuring economic processes and political activities. -
Google Benefit from News Content
Google Benefit from News Content Economic Study by News Media Alliance June 2019 EXECUTIVE SUMMARY: The following study analyzes how Google uses and benefits from news. The main components of the study are: a qualitative overview of Google’s usage of news content, an analysis of news content on Google Search, and an estimate of revenue Google receives from news. I. GOOGLE QUALITATIVE USAGE OF NEWS ▪ News consumption increasingly shifts towards digital (e.g., 93% in U.S. get some news online) ▪ Google has increasingly relied on news to drive consumer engagement with its products ▪ Some examples of Google investment to drive traffic from news include: o Significant algorithmic updates emphasize news in Search results (e.g., 2011 “Freshness” update emphasized more recent search results including news) ▪ Google News keeps consumers in the Google ecosystem; Google makes continual updates to Google News including Subscribe with Google (introduced March 2018) ▪ YouTube increasingly relies on news: in 2017, YouTube added “Breaking News;” in 2018, approximately 20% of online news consumers in the US used YouTube for news ▪ AMPs (accelerated mobile pages) keep consumers in the Google ecosystem II. GOOGLE SEARCH QUANTITATIVE USAGE OF NEWS CONTENT A. Key statistics: ▪ ~39% of results and ~40% of clicks on trending queries are news results ▪ ~16% of results and ~16% of clicks on the “most-searched” queries are news results B. Approach ▪ Scraped the page one of desktop results from Google Search o Daily scrapes from February 8, 2019 to March 4, 2019 -
Predicting the Present with Google Trends
Predicting the Present with Google Trends Hyunyoung Choi, Hal Varian∗ December 18, 2011 Abstract In this paper we show how to use search engine data to forecast near-term values of economic indicators. Examples include automobile sales, unemployment claims, travel destination planning, and consumer confidence. Government agencies periodically release indicators of the level of economic activity in various sectors. However, these releases are typically only available with a reporting lag of several weeks and are often revised a few months later. It would clearly be helpful to have more timely forecasts of these economic indicators. Nowadays there are several sources of data on real-time economic activity available from private sector companies such as Google, MasterCard, Federal Express, UPS, Intuit and many others. In this paper we examine Google Trends, which is a a real-time daily and weekly index of the volume of queries that users enter into Google. We have found that these query indices are often correlated with various economic indicators and may be helpful for short-term economic prediction. We are not claiming that Google Trends data can help in predicting the future. Rather we are claiming that Google Trends may help in predicting the present. For example, the volume of queries on automobile sales during the second week in June may be helpful in predicting the June auto sales report which is released several weeks later in July. It may also be true that June queries help to predict July sales, but we leave that question for future research, as this depends very much on the particular time series in question. -
Searching for Information Online: Using Big Data to Identify The
C O R P O R A T I O N Searching for Information Online Using Big Data to Identify the Concerns of Potential Army Recruits Salar Jahedi, Jennie W. Wenger, Douglas Yeung SUMMARY ■ In this report, we assess some Key findings empirical applications of web search data and discuss the prospective value such data can offer to Army recruiting • Google search queries can be used to better under- stand how interest in military careers has evolved over efforts. We discuss three different tools—Google Trends, time and geographic location. Google AdWords, and Google Correlate—that can be used to access and analyze readily available, anonymous • It is possible to use these tools to identify the chief data from Internet searches related to the Army and to Army-related concerns that potential recruits have, Army service. We find that Google search queries can be including the qualifications for, procedures for, or ben- efits of enlisting. used to better understand how interest in military careers has evolved over time and geographic location, and even • It is possible to predict with reasonable accuracy what identify the foremost Army-related concerns that potential individuals were searching for months before or after recruits experience. Moreover, it is possible to predict with searching for Army-related terms reasonable accuracy what non-Army related terms people • Including Google Trends terms in a model of factors are searching for in the months before or after an Army influencing the number of Army accessions increases query. Finally, our results suggest that search terms can the predictive power of the model. -
Using Google Trends in Management Fashion Research: a Short Note Dag Øivind Madsen
Using Google Trends in Management Fashion Research: A Short Note Dag Øivind Madsen To cite this version: Dag Øivind Madsen. Using Google Trends in Management Fashion Research: A Short Note. 2016. hal-01343880 HAL Id: hal-01343880 https://hal.archives-ouvertes.fr/hal-01343880 Preprint submitted on 10 Jul 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. USING GOOGLE TRENDS IN MANAGEMENT FASHION RESEARCH: A SHORT NOTE Dag Øivind Madsen, University College of Southeast Norway Abstract Google Trends (GT) is an analytic tool for measuring and monitoring Internet search data. In recent years GT been utilized for research in fields as diverse as health care, political science and economics. This short paper looks at the possibilities of using GT in management fashion research. GT could have a natural application in the study of management fashion. After all, a key question of interest to management fashion researchers is how the popularity of management concepts and ideas evolves over time. The paper discusses the pros and cons of using GT in management fashion research. Using Internet search data can possibly reveal intentions and expectations in the management fashion market, provide indicators of future fashion demand, and as well as indicate “outbreaks” of contagious management concepts and ideas. -
ADVANCED OUTDOOR BAKING Foil Cooking Dutch Oven Box
ADVANCED OUTDOOR BAKING August Learning Events – Thursday, August 9, 2007 Trainers: Barb Anzalone & Lizbeth Kohler OBJECTIVE: To provide participants with hand’s-on opportunities to try delicious recipes and various outdoor baking methods. Foil Cooking Bake Packer * PINEAPPLE UPSIDE DOWN CAKE * BAKEPACKER DUTCH TREAT Gingerbread In An Orange Apple Coffee Cake Baked Apples Dutch Baby Pancake Dutch Oven Novelty Cooking * BROWN BEAR IN APPLE ORCHARD * TRAIL BROWNIES * SHIPWRECK BREAKFAST Arm Pit Fudge Country Breakfast Gorp Balls Monkey Bread Ice Cream Chocolate Delight Snack O'Crackers Grandma’s Apple Brown Betty Dump Cake Cobbler Reflector Oven Heath Bar Brownies Frying in a Dutch Oven Pioneer Pocket Pies Quick Scones Pie Irons * FRENCH BREAKFAST PUFF Box Oven Chocolate Croissants * CHEESE CAKE * PIGS IN BLANKETS Pound Cake S’mores Chocolate Pretzel Rings Stick Cooking Cookies *CAKE KEBABS Pigs in Blankets Bread on a Stick Cupcakes Dough Boys Apple Pie on a Stick Buddy Burners Pineapple Upside Down “Can" Cakes * Recipes tested at 2007 August Learning Events BAKING WITH FOIL PINEAPPLE UPSIDEDOWN CAKE • 12 cake donuts • 1 1/4 cups brown sugar • 1 1/4 sticks butter, softened • 12 pineapple rings Slather butter on donuts. Pack on brown sugar. Top with pineapple. Seal tightly in foil packet, and cook on coals for 5 minutes. Orange Cup Gingerbread Six or seven oranges Your favorite gingerbread mix (you can use any kind of boxes cake mix: white, chocolate, blueberry muffin - be creative) Hollow out the oranges from the top making sure that you do not cut a hole in the orange (other than the top). Fill the orange halfway to the top with gingerbread batter.