Take the Fourth Read online

  Take the Fourth

  Jeffrey Walton

  AuthorHouse™

  1663 Liberty Drive

  Bloomington, IN 47403

  www.authorhouse.com

  Phone: 1-800-839-8640

  © 2011 Jeffrey Walton. All rights reserved.

  No part of this book may be reproduced, stored in a retrieval system, or transmitted by any means without the written permission of the author.

  First published by AuthorHouse

  ISBN: 978-1-4520-8928-7 (sc)

  ISBN: 978-1-4520-8929-4 (hc)

  ISBN: 978-1-4520-8930-0 (ebk)

  Library of Congress Control Number: 2011900409

  Printed in the United States of America

  Any people depicted in stock imagery provided by Thinkstock are models,

  and such images are being used for illustrative purposes only.

  Certain stock imagery © Thinkstock.

  Because of the dynamic nature of the Internet, any Web addresses or links contained in this book may have changed since publication and may no longer be valid. The views expressed in this work are solely those of the author and do not necessarily reflect the views of the publisher, and the publisher hereby disclaims any responsibility for them.

  Contents

  Preface

  Chapter 1

  Chapter 2

  Chapter 3

  Chapter 4

  Chapter 5

  Chapter 6

  Chapter 7

  Chapter 8

  Chapter 9

  Chapter 10

  Chapter 11

  Chapter 12

  Chapter 13

  Chapter 14

  Chapter 15

  Chapter 16

  Chapter 17

  Chapter 18

  Chapter 19

  Chapter 20

  Chapter 21

  Chapter 22

  Chapter 23

  Chapter 24

  Chapter 25

  Chapter 26

  Chapter 27

  Chapter 28

  Chapter 29

  Chapter 30

  Chapter 31

  Chapter 32

  Chapter 33

  Chapter 34

  Chapter 35

  Chapter 36

  Chapter 37

  Chapter 38

  Chapter 39

  Chapter 40

  Chapter 41

  Chapter 42

  Chapter 43

  Chapter 44

  Chapter 45

  Chapter 46

  Chapter 47

  Chapter 48

  Chapter 49

  Chapter 50

  Chapter 51

  Chapter 52

  Chapter 53

  Chapter 54

  Chapter 55

  Chapter 56

  Chapter 57

  Chapter 58

  Chapter 59

  Chapter 60

  Chapter 61

  Chapter 62

  Chapter 63

  Chapter 64

  Chapter 65

  Chapter 66

  Chapter 67

  Chapter 68

  Chapter 69

  Chapter 70

  Chapter 71

  Chapter 72

  Chapter 73

  Chapter 74

  Epilogue

  There would be a few more trees left standing, one less book on the shelf, and a ton of ideas still bouncing around in my head like lotto balls, if it wasn’t for the daily doses of support and patience that I received from my loving wife Wendy. Through her encouragements and perseverance I took my first steps of placing keystrokes to liquid crystal displays and developing characters, plots, and subplots and eventually turning them into this novel before you. I thank her for that. I love her for that and for that I dedicate these bounded words to her, my Wendy. These words are as much hers as they are mine.

  “The right of the people to be secure in their persons, houses, papers, and effects, against unreasonable searches and seizures, shall not be violated, and no warrants shall issue, but upon probable cause, supported by oath or affirmation, and particularly describing the place to be searched, and the persons or things to be seized.”

  Preface

  An excerpt from a thesis entitled “DATA”

  A visionary is one who can foresee the realization of technologies based upon innovations and inventions that do not exist today.

  Joseph Woodland was a visionary. In 1949, while relaxing in his beach chair, he invented the barcode by placing Morse code in the sand with his fingers. There were no barcode readers or lasers at this time and computers were in their infancy stage. Over time the new technologies bloomed and the barcode made strides. Lasers became scanners, scanners became fingers, and the barcode became a language, a language spoken throughout the world, by computers throughout the world.

  Look around today, barcodes, barcodes, barcodes, on milk, DVD’s, FedEx packages, assembly lines, luggage, bottled water, drugs, cars, furniture, greeting cards, t-shirts, passports, lumber, and even people. A series of lines in the sand becomes the universal methodology of tracking—tracking what we buy, where we are going, when we do so, how we do it, and who we are. Each time a barcode is read it becomes raw data—raw data analyzed in the most mundane way, raw data analyzed in the most personal way.

  Think about it. At the grocery store, the shopping cart becomes a basket of information. Scan a bag of potato chips and the price automatically appears on the screen with the total. Scan a bag of potato chips and the computer will delete an entry out of its inventory. If the shelf inventory is low a message automatically appears on the LCD window display of the stock boy’s PDA. If store inventory falls below a predetermined amount it will automatically file an order for more potato chips with the purveyor. Information provided to the purveyor will be used in ranking the store’s profit margin based on its product of potato chips. This in turn will be used to determine delivery priorities. The more the store sells, the faster they receive their product. Scanning saves the store money, less time for error, no need to call in orders, no need to do inventory. Scan the bag of chips and handed with the receipt are coupons for future purchases. These coupons could be for the same product or its competitors, buy Coke and get a Pepsi coupon, buy Lays get a Herr’s coupon. Competitors use this information to gain insights to their market campaigns and insights into their rivalries.

  Harmless information, 1’s and 0’s, are just sitting on a database in the middle of Kansas somewhere. Harmless information used by the store to help run its store. At any exact moment in time, inventory can be taken and supply statistical information on product sales and profit margins. Decisions can be made whether to increase shelf space for a particular product or remove a product altogether, find out if the in store bakery is churning out dough or stuck in dough, if fish sales actually increase on Fridays, whether or not to put those 40 cases of almost flat soda on sale based on its limitation of shelf life, or how many dozen eggs were sent back based on expiration date. They can predict the future, project inventory for next week, next month, even next year, all by analyzing their raw data.

  Now scan a supersaver card, punch in a phone number, pay by debit/credit card, swipe a finger, have a retinal scan (not rectal mind you, though that would make checkout lines a little more interesting) and this bag of potato chips becomes linked to an individual along with the date and time. Raw data becomes information; information transformed into who bought what, where, and when.

  Along with the Herr’s bag of potato chips, Marcy Peterson purchases Little Debbie Snack Cakes, 3 Lean Cuisine’s frozen dinners (meatloaf, lasagna, smoked turkey), two 2 liter diet Cokes, loaf of Wonder bread, 24oz jar of Skippy peanut butter, Dial soap, Kotex, bottle of Advil, mint flavored Scope, two pi
nts of Ben & Jerry’s (Cherry Garcia, Fudge Brownie), 40 watt GE light bulbs 2-pack, string cheese, a Starr magazine, and a pack of Tic-Tacs. Marcy’s total $129.86 deducted from her Fidelity checking account on July 18, 2005, at 5:45p.m. in the Winn Dixie, Jacksonville, Florida, store #23345. Marcy uses one $1.00 coupon.

  With this data, a store can pinpoint its customers’ demographics. What’s the gender majority? What’s the average age of the customers? What’s the average income? Who’s the best customer? What’s their average spending per visit? How much did they spend last year? Do they use coupons? Food stamps? Are they married? Have children? With this data, they can ask themselves—Should a pharmacy be installed, buy more carts with child seats? Should the fresh vegetables and fruits be upgraded? Sell a cheaper grade of hamburger? Add more family only parking? Charge a fee for bank withdrawals when they use a debit card? Add more types of shampoo? Change their magazine types at the checkout counters? Add a self-checkout?

  Now grant access to the database with outside vendors, let them query to their hearts content—for a price of course, and here is where the scary part starts.

  This same information, the same 1’s and 0’s sitting in the middle of Kansas can become personal without knowing it. That same Marcy applies for some health insurance. She calls on the phone and is connected to a salesperson. After taking some general information, the computer whirls, spins, chugs, and spits back a monthly dollar amount all within less than a minute. The processing power behind this monthly dollar amount is staggering. It used to be just a quick look-up in the actuary tables based on age and race, not anymore. It’s a very complex process, basically a computer program, broken into many parts. The first part of this massive thinking machine deals with the entered data—name, telephone number, social security number, date of birth. This data is used for search parameters, search parameters for databases; many databases, one of them being the Winn Dixie database in the middle of Kansas somewhere. The information stored there is harmless—who bought what, where, and when, but in this specific case the search parameters are: Marcy and all items bought within the last year. The raw data is returned—it’s just mindless information to the naked eye. Most people wouldn’t give it a second glance if they saw the information scattered on a piece of paper—just a bunch of numbers and names, no big deal. The second part of this complex process is the data scrubbing; turning this mundane information into something of use. From analyzing the data (another part of this computer program and the most complicated of them all) it can be determined that Marcy Peterson lives alone and buys food for roughly 3 days during each trip to the grocery store. Her average cost per shopping trip: $65.12. She rarely buys fresh fruits, vegetables, and never buys fish. She mainly purchases prepackaged meals and snacks with an average daily consumption of 3589 calories per day. Checking other databases and a search for any type of health club membership turns up empty. Last time she bought a pair of sneaks was 2 ½ years ago. Clearly Marcy is not health conscious and there is an 82.34% chance that she is overweight. Cha-ching—increase base insurance amount by 33%. Combined this information with her yearly driving distance and any traffic violations reported at the local DMV, and it is assumed she is a safe driver—deduct 1.5% off the base amount. Next, incorporate the type of work she does via her social security number, her Friday night bar bills or liquor bills taken from her Master Card, any doctor visits taken from her PPO card, or find out if she’s a non-smoker and the base amount fluctuates up and down depending on the outcome of each search.

  Raw data can become extremely personal as well. A vendor such as Johnson & Johnson can query a store’s database and access one or all customers. They can find the average sales per customer, per age group, per visit, per year, per product, or products. They can determine the results of their most recent ad campaign per anyone of the demographic statistics thus helping them to analyze their marketing scope and in turn formulate new strategies to increase sales. Anyone can see the benefits to this information—these bits of 1’s and 0’s sitting in the middle of Kansas somewhere. Information is the key to success and marketing gurus will take advantage of this fact. Say J & J might want to convert a user of a competing product to their brand under a new marketing campaign, how about Kotex to their Stayfree.

  Querying that same Winn Dixie database we find Marcy Peterson once again. She last bought Kotex on July 18th. She uses the competition but does she always? A simple query would suffice. Turns out, the answer is yes and with the enclosed dollar coupon. In the database is Marcy’s demographics file containing address, age, phone number, etc… Presto—send a coupon; send all users of Kotex a coupon in the mail, maybe even a free sample. Better yet, a timely coupon so it is fresh in the memory and not lost or thrown away. Querying the database again yields that Marcy Peterson buys Kotex on average once every 4 to 5 weeks and using a simple calculation it can be surmised with a 96% accuracy that her next period will fall somewhere on the week of August 15th, therefore to assure the maximum benefits of a marketing campaign, the coupon should be sent no earlier than a week before her period. She gets her coupon on the 12th and uses it on the 16th, all without ever knowing that the big conglomerates are watching and learning from her every move, they even know when she’s menstruating. The timely campaign is a success with a 62.76% switch rate just by sending a coupon. Follow it up with one or two more and hopefully buying Stayfree becomes a habit and gains a customer for life or at least until menopause.

  . . .

  Chapter 1

  She heard tires squeal, the roar of the engine, and looked in that direction. A dark car could have been blue or black, hard to tell with the sunlight and shade of the trees during this afternoon. It was one of those old muscle cars, Nova, Camaro, again hard to tell, she wasn’t an expert in automobiles; they got her from point A to point B; that was all that mattered, even though she drove the top of the line Mercedes sedan. The car she saw had big fat tires, that she remembers and a vinyl roof—black, nothing else stood out in the mind. She watched it as it sped down the street, five maybe ten seconds it was gone. “Humph,” such an asshole she thought, “speeding around a children’s playground, should string him up, they should have put in speed bumps”. She was pissed.

  As the echo of the car wafted in the summer air, her ears and mind came back into focus. She heard children crying, startled by the sounds of the loud asshole no doubt. She turned to her little ones, picked up Samuel who was crying and looked for Ripley who was playing on the gym, fort thingy that was bigger than some people’s house—cost more too. She scanned the horizon and Ripley’s pink tee shirt was nowhere to be found. She scanned again, looking at the swings, then to the sliding board, still no luck. She moved closer to the fort area hoping to get a glimpse that she was inside.

  “That’s an unusual name?”

  “She was named after Ellen Ripley.”

  “Who?”

  “Well actually after Sigourney Weaver.”

  “Sorry I still don’t follow you.”

  “Sigourney Weaver’s character in Alien; one of my husband’s favorite movies, he just loved the name Ripley.”

  And so the questioning by the detective continued.

  . . .

  Chapter 2

  Gunfire. Automatic gunfire. Screams. Blood curdling screams. Shattering glass. More automatic gunfire. Ricochets. More screams. More blood curdling screams. More shattering glass. Even more screams. Sounds of horror. More screams. More gunfire. Panic. Panic. Panic. Utter pandemonium. Running footsteps. Trampling footsteps. The sounds of the terrified. The sounds of war. Crying. Sobbing. The sounds of despair, grasping for air. Crying. Sobbing.

  The bullets stopped. The loud and deafening sounds stopped. For a brief second, as the confusion began to set in, almost complete silence fell upon the area, then the wailing and screams of the injured became predominant as they drowned out the song “Winter Wonderland” playing over the mall’s speaker system. There were bodies and blood everywhere, in fro
nt of the Modell’s sporting goods store, in front of Macy’s, in front of the shoe store, in front of the jewelry store, in front of the pretzel stand, in front of Starbucks. There were bodies and blood everywhere. There was a mother who was bleeding profusely, holding her dead son. An elderly man who was shopping for his grandchildren was clinging to his dead wife. An entire family lay bullet stricken with the mother still clutching their smiling family photo with Santa taken moments ago. There was a set of twins in strollers with no more life to give along with a proud father whose life had also been extinguished while the mother laid unconscious and unaware of the horrors that lie ahead. There were teenagers with iPods who will no longer experience one of life’s little pleasures, that of song. There was a twenty-something who hadn’t taken her first sip of a mocha double latte. There was an Asian couple who will not see the birth of their first child. There was a young man holding a ring box for a very special Christmas present who will never hear the word “Yes”. There were people with presents who will never feel the joy of giving. There were people who will never feel the joy of receiving from a loved one. There were people who were in too much of a rush to say “I love you” before heading to the mall. There were people who were full of life and the Christmas spirit just moments ago. There were bodies and blood everywhere.

  There were seventy-eight people in total who will not see the New Year, sixty-two of those people died in a mall outside of Philadelphia just two days before Christmas, the others all died of complications shortly thereafter.

  Two of the seventy-eight people who perished in the nonsense were the assailants themselves. There were five in total. These two walked out of the garage holding a plastic clothing bag, the kind one receives during checkout with a new suit or jacket, and then walked calmly up the stairs to the top level of the mall right past California Pizza Kitchen and Bloomingdales. They staked out a claim at the other end of the mall, right in front of Macy’s department store. It was a busy intersection of the mall with views of both the upper and lower decks and very close to the parking garages. There were plenty of people here at any given time of day, even more so since it was the season. They checked their watch. Right on time—a few seconds later the mall echoed with gunfire and screams.