aas (a는 angle, s는 side) AAS합동, 삼각형의 두 각과 끼인 변이 아닌 한 변의 길이가 같아도 삼각형은 합동이라는 것 (두 각이 같으면 나머지 한 각도 같으므로 결국 ASA와 동일)
abscissa 가로축
absolute value 절대값
acute angle 예각
addition inverses addition은 덧셈 inverse는 역,역수,역원 그러나 덧셈에 대한 역원은 addition inverse가 아니라 additive inverse 라고 한다 (x의 덧셈에 대한 역원은 –x)
addition property 방정식의 양변에 같은 식을 더해도 식이 성립함

adjacent angles 이웃한 각
adjacent arcs arc는 호이므로 인접한 호
adjacent leg 인접한 leg, leg항 참조
a line and a plane are parallel 선과 평면이 평행하다
a line and a plane are perpendicular 선과 평면이 수직이다
alternate interior angles 엇각
altitude 높이(수선), 예를 들어 삼각형의 높이라고 하면 꼭지점에서 대응변에 내린 수선을 의미
angle 각
angle of depression 내려본 각
angle of elevation 올려본 각
apothem of a regular polygon 정다각형에서 중심과 변의 중점을 연결한 선분
area 면적
area of rectangle 직사각형의 면적
arroe notation 오타가 아니신지? 
asa ASA 합동인 듯
auxiliary line 보조선
axes axis(축)의 복수
axioms 공리
axis of xymmerty 역시 오타인 듯 axis of symmetry 라면 대칭축
base 밑변, 밑각, 밑면 등
base angles 밑각
biconditional 겹조건문
binomials 이항식, binomial은 이항의(항이 2개인)란 의미
bisector of a angle 각의 이등분선
bisector of a segment 선분의 이등분선
boundary 경계, 둘레
box-and-whisker plots 통계에서 쓰는 그림 중 하나, 최대값,최소값,중앙값,사분위수 등을 가지고 나타냄 (통계는 잘 모르니 패스)
center 중심
center angle 중심각
center of a regular polygon 정다각형의 중심
chord 현
cimilar solids similar solids인 듯, 닮은 입체
circle 원
circumference 원주, 원둘레
circumscribed about the circle 원에 외접하는
circumscribed about the polygon 다각형에 외접하는
closed half-planes 닫힌 반평면
coefficient 계수
collonear points 오타로 추정 collinear points는 같은 직선 위의 점들
combined variation 결합 변분
commmon factor m이 3개는 곤란합니다 common factor 공통인수
common tangent  공통 접선
complementary 보, 여, 나머지의
complementaty angles 오타 complementary angles 여각
completing the square 제곱식 만들기

composite 합성 (예. Composite function 합성함수)
compound statments compound statements 복합 명제
concentric circle 동심원
concentric sphere 동심의 구
conclusion 결론
conditionals 조건문(들)
conditional statements 조건부 명제
cone 원뿔, 원추
congruence mapping 합동 사상
congruent 합동의
congruent angles 합동의 각, 이하 생략 (arc, circle, segment 항 참조)
congruent arcs 
congruent circle 
congruent segment 
conjunction 논리곱
consecutive even integers 연속되는 짝수 (even이 짝수의, integer는 정수)
consecutive integers 연속한 정수 
consecutive odd integers 연속한 홀수
constant 상수
constant monomial 상수 단항식
constnat of proportionality 아마 constant of proportionality 인 듯, 비례 상수 

constant of variation 변분 상수
contraction 줄임
contrapositive 대우(의) 
converse 역 (역 명제 라고 할 때의 역, inverse의 역과는 의미가 다름)
convex polygon 볼록 다각형
coordinate 좌표
coordinate axes 좌표축
coordinate plane 좌표 평면
coplanar points 동일 평면상의 점
corollary 따름정리
corresponding angles 동위각
correnponding sides 오타 corresponding sides 대응변
cosine 코사인 --;;
cost 비용
counterexample 반례
cubic equation 삼차 방정식
cylinder 원기둥
deductive reasoning 연역적 추론
degree measure 글쎄요, degree는 각도 또는 차수라는 의미를 가짐
degree of a monomial 단항식의 차수
degree of a polynomial 다항식의 차수
degree of a variable in a monomial 단항식내의 변수의 차수
diagonal 대각의
diameter 지름
dilation 확대(변환)
direct variation 정비례
disjunction 논리합
distance from a point to a line 점에서 직선까지의 거리
distance travled 만약 distance traveled 라고 치면 이동한 거리
distributive property 방정식을 풀 때 분배 법칙 사용 
divided proportionally 비례하여 나뉜 이라고 해야 되려나
divisible 나누어지는 (나누어 떨어지는)
division property 구체적으로 뭘 의미하는 건지 모르겠지만 나눗셈 성질
domain 정의역
dot pruduct 오타 dot product 내적
double 이중, 동사라면 2배로 하다 또는 2배가 되다
ending --;;
endpoint 끝점
equal 같다
equation 방정식
equiangular 각이 같은, 등각의 
equilateral 등변의
equivalent 동치인
evaluating the expression_(72, 162, 255); font-family: 돋움; ">식의 값을 구함
even integer 짝수
evenly divisible 똑같이 나뉘어지는
event 사건 (확률 등에서)
expansion 확대, (식의) 전개
exponent 지수
exponential form 지수함수 꼴
extremes 극값
factored completely factor는 인수, 동사로는 인수분해하다 라는 의미를 가짐
factors 인수, 인자
factor set 인자 집합
finding the value of the expression_(255, 51, 153); font-family: 돋움; ">식의 값을 찾음
finite decimal 유한 소수
formulas 공식
fractal geometry 프랙탈 기하 (프랙탈은 번역하지 않고 쓰이는 용어)
fractional equation 분수 방정식
frequency distribution 도수 분포
function 함수
functional notation 함수 기호법
geometric mean 기하 평균
glide reflection 미끄럼 반사 변환
glide reflection symmetry 미끄럼 반사 변환에 대한 대칭
graph 그래프 --;;
graph of an equation 방정식의 그래프
graph of the inequality 부등식의 그래프
graph of the orderd pair 오타 graph of the ordered pair 순서쌍의 그래프
great circle 대원
greatest common factor (GCF) of two or more integers 둘 또는 그 이상의 정수의 최대공약수
greatest monomial factor of a polynomial 다항식의 최대 단항 인수
greatest value 최대값
grouping symbol 그룹은 군이라고 보통 번역하지만 그건 아닐테고, 무슨 기호일까요? --;;
heavy type 모르겠습니다
height 높이
heron's formula 헤론의 공식 (세 변의 길이로 삼각형 넓이를 구하는 공식이었던가?)
histogram 히스토그램 --;;
hl 난해하군요 --;;
horizontal axis 수평축
hundreds digit  digit 은 숫자나 자릿수를 의미, 백의 자리수 정도 될까 (불확실)
hypotenuse 빗변
hypotenuse-acute angle method 직각삼각형에서 빗변과 한 예각이 같으면 합동이다 라는 것

여기선 RHA 합동, 미국에선 그냥 HA 합동이라고 할 겁니다.
hypothesis 가설
identity 항등식, 항등원, 단위원소
identity element for addition 덧셈에 대한 항등원
identity element for multiplication 곱셈에 대한 항등원
if-then statement statement는 명제이므로 ..이면..이다 라는 형식의 명제를 말하는 듯
image 상
indirect proof 간접증명법
inequality 부등식
inductive reasoning 귀납적 추론
infinite 무한의
inscribed angle 원주각
inscribed in a circle 원에 내접하는
inscribed in the polygon 다각형에 내접하는
in simplest form 가장 단순한 형태로 (공통 약수가 없는, 즉 기약분수 상태로)
integers 정수
intersection 만남, 교집합, 교점
invers 오타 inverse 역, 역수, 역원 또는 이 명제를 의미한다 (역, 이, 대우 참조)
inverse variation 반비례
inverse variation as the square 제곱에 반비례
irrational numbers 무리수
irreducible 기약, 더 이상 나눌 수 없는
isometry 등장 변환
isosceles trapezoid 등변 사다리꼴
joint variation variation(정비례나 반비례 등)이 섞였다는 의미일 듯 한데 잘은 모르겠음
lateral angle lateral은 옆이란 의미
lateral faces 옆면
later area 옆면적
later edges 옆변 (모서리)
left=less than 사이에 있는 건 이퀄입니까?? 그렇다면 좌변이 (우변보다) 작거나 같은 정도?
leg-acute angle method 짐작이 안 가는군요
leg-leg method 마찬가지
legs 사다리꼴에서 평행하지 않은 변이나 직각 삼각형에서 빗변이 아닌 변
length 길이
like 닮은 참고로 like terms 는 동류항 
linear equation 선형(일차) 방정식
linear equation in standard form 표준형의 일차 방정식
linear equation not in standard form 표준형이 아닌 일차 방정식
linear function 일차 함수
linear term 일차항
line segment 선분
line symmetry 선대칭
locus 자취
logically equivalent 논리적으로 동치인
lsosceles triangle 오타 isosceles triangle 이등변 삼각형
magnitude 크기
major arc 긴 호 (우호)
mapping 사상
maximum point 최대점
means 평균
measure of a major arc 180도보다 크다 
measure of a minor arc 180도보다 작다
measure of a semicircle 180도
median 중앙값
midpoint of a segment 선분의 중점
minimum point 최소점
minor arc 열호
mode 모드, 최빈수, 최빈값
monomial 단항식
multiple root 중근
multiplication property 방정식의 양변에 같은 식을 곱해도 식이 성립함
multiplicative inverses 곱셈에 대한 역원
negation 부정
negative integers 음의 정수
negative number 음수
negative side 여기서 side가 무슨 의밀지... 식의 좌변, 우변 할 때의 '변'도 side라고 합니다
no current 수학 용어는 아닐테고 흐름이 없는? (강물에서 배의 속력 문제 같은 데서 나온 듯)
nonlinear equation 비선형 방정식
nonterminating 무한의
nth power of b b의 n제곱
numerical coefficient 숫자인 계수
numerical expression_(0, 33, 176); font-family: 돋움; ">수치 방정식
oblique prism 빗각기둥
obtuse angle 둔각
odd integers 홀수
open half-planes 열린 반평면
open sentences 열린 문장
oppposite p가 너무 많습니다 opposite 반대의
opposite leg 흐음
opposite rays 반대방향 반직선
one-to-one mapping 일대일 사상
one-variable equation 일변수 함수
ordered pair 순서쌍
ordinate 세로좌표
origin 원점
parabola 포물선
parallel 평행한, 병렬의
parallel lines 평행선
parallelogram 평행사변형
parallel planes 평행면
perfect square 완전제곱
perfect square trinomials 삼항식의 완전제곱(공식) : x^2 ± 2ax + a^2 = (x±a)^2
perimeter of rectangle 직사각형의 둘레(의 길이)
periodic 주기의
perpendicular 수직, 수선, 직교
perpendicular bisector 수직 이등분선
perpendicular lines 수직선
plane symmetry 면대칭
plot 좌표에 따라 점을 찍거나 연결하여 선을 그린다
point of tangent 접점
point symmetry 점대칭
polygon 다각형
polynomial 다항식
polynomial equation 다항 방정식
positive integers 양의 정수
positive number 양수
positive side 양의 변 (변이 기하학적인 것인지, 식의 양변을 말하는 것인지 불확실)
postulates 공준
preimage 원상
prime 소수의, 서로 소의
prime factorization 소인수분해
prime numer 소수
prime polynomial 기약 다항식
principal 주된
prism 각기둥
product 곱, 적
proof 증명
properties of congruence 합동의 성질(뭔가 어색)
properties of equality 등식의 성질
proportion 비례
pyramid 각뿔, 피라미드
pythagoream triple 오타 Pythagorean triple 피타고라스 정리를 만족하는 3개의 수 
quadrants 사분면
quadratic direct variation 2차 정비례(?)
quadratic equation 이차 방정식
quadratic formula 이차방정식의 근의 공식
quadratic function 이차 함수
quadratic polynomial 이차 다항식
quadratic term 이차항
radical equation 무리 방정식
radicand 피제곱근수
radius 반지름
radius of a regular polygon 정다각형의 반지름
random experiment 확률(랜덤) 실험
range 치역
ratio 비, 율
rationalizing the denominator 분모를 유리화
rational number 유리수
ray 반직선
real number 실수
reciprocals 역수, 상반
rectangle 직사각형
rectangle region 직역하면 직사각형 영역
reflection 반사
reflexive property 반사적 성질(?)
regular polygon 정다각형
regular pyramids 정각기둥
regular square pyramids 정사각기둥
relation 관계
repeating 순환하는 예. Repeating decimal 순환 소수
rhombus 마름모
right angle 직각
right cylinder 직원기둥
right=greater than 위의 left 항 참조 (부등식을 읽는 법인 게 아닐까 짐작)
right prism 직각기둥
right triangle 직각삼각형
root 근
rotation 회전
rotational symmetry 회전에 대한 대칭
same-side interior angles 같은 변의 내각(?)
sas 아마도 SAS 합동
satisfy 조건을 만족시키다
scalar multiple 스칼라배 (스칼라만큼 곱함)
scale factor scale은 눈금이라는 의미겠지만…
scientific notation 유효숫자 표기법
secant 시컨트 (삼각함수의 sec)
sector of a circle 원의 부채꼴
segment 선분, 활꼴
semicircles 반원
side 변
similar 닮은
similarity mapping 닮음 변환
similar triangles 닮은 삼각형
simple event 단순 사건(?)
simple form 간단한 형태
simple space 알 수 없음
simplest form 간단한 형태 (분수에서 분자 분모의 공통 약수가 없는 상태)
simplified 단순화된
simplifying expression_(204, 153, 0); font-family: 돋움; ">란 간단히 하다, expression_(68, 68, 68); font-family: 돋움; ">
simplifying the expression_(204, 153, 0); font-family: 돋움; ">식을 간단히 함
sine 싸인 (삼각함수의 sin)
skew lines 꼬인 위치의 직선
slant height 비탈 높이
slope 기울기
slope-intercept form 직선의 방정식을 기울기와 절편으로 나타낸 형태, y = ax + b 의 형태 
solution 해, 풀이
solution of the inequality 부등식의 해
solution set 해집합
solution set of the inequality 부등식의 해집합
solve a conjunction 논리곱을 풀다
solve a disjunction 논리합을 풀다
solved solve가 풀이하다 이므로 대충 맞춰서 해석, 뭔가 다른 의미가 있는지는 모름 
solving 위와 동일
space 공간
sphere 구, 구면
square 정사각형, 제곱
square root 제곱근
sss 삼각형의 SSS 합동
standard form 표준형
stem-and-leaf plot 줄기-잎 그림 (통계에서 데이터를 나타내는 방법 중 하나)
straight angle 평각
substitution property 아마 방정식 풀이 시 x 대신 숫자를 대입해 확인하는 것을 말하는 듯
subtraction property 방정식의 양변에 같은 식을 빼도 식이 성립함
sum 합
supplement 보충하다 등의 의미, 명사로선 보각
supplementary angle 보각
symmetric property 대칭 성질
symmetry 대칭
system of equations 연립 방정식
tangent 탄젠트, 접선
tangent circle 접원
tens digit 아마도 십의 자리수 (불확실)

terminating 유한의  예. terminating decimal 유한 소수 
terms 항(들)
theorem 정리
total area 전체 면적
transformation 변환
transitive property 추이 성질
translation 평행이동
translational symmetry 평행이동에 대한 대칭
transversal 횡단선, 횡단적, 가로지름선
trapezoid 사다리꼴
triangle 삼각형
trigonometric functions 삼각함수
trigonometric ratios 삼각비
trinomials 삼항(의)
truth table 진리표
two-variable equation 이변수 방정식
unending 끝이 없는?
units digit 1의 자리 수
valid argument 직역하면 유효한 변수이지만서도…
value 값
value of the expression_(21, 98, 0); font-family: 돋움; ">식의 값
values of the function 함수의 값
values of the variable 변수의 값
variable 변수
variable expression_(21, 98, 0); font-family: 돋움; ">변수로 나타낸다는 뜻인지 변수를 포함한 식이란 의민지...(불확실) 
variance 분산
vector 벡터
venn diagram 사람 이름이니 대문자로 Venn diagram 벤다이어그램
vertical angle 맞꼭지각
vertical axid 오타 vertical axis 수직축
vertex 꼭지점
whole numbers 범 자연수 (자연수와 0), 국내에선 보통 음이 아닌 정수 라고 표현합니다
x-axis x 축
x-coordivnate 오타 x-coordinate x 좌표
x-intercept x 절편
y-axis y 축
y-coordinates y 좌표
y-intercept y 절편
1 mi/h current 시속 1마일의 흐름

출처 - 네이버 지식인 : http://goo.gl/6aaY0

A time series database server (TSDS) is a software system that is optimized for handling a time series. In this context, a time series is an associative array of numbers indexed by a datetime or a datetime range. These time series are often called profiles or curves, depending upon the market. A time series of stock prices might be called a price curve, or a time series of energy consumption might be called a load profile. Despite the disparate naming, the operations performed on them are sufficiently common as to demand special database treatment.

TSDSs simplify the development of software with complex business rules in a wide variety of sectors. Queries for historical data, replete with time ranges and roll ups and arbitrary time zone conversions are difficult in a relational database. Compositions of those rules are even more difficult. This is a problem compounded by the free nature of relational systems themselves. Many relational systems are often not modelled correctly with respect to time series data. TSDS on the other hand impose a model and this allows them to provide more features for doing so.

Ideally, these repositories are often natively implemented using special database algorithms. However, good performance has also been obtained by storing time series as binary large objects (BLOBs) in a relational database or by using a VLDB approach coupled with a pure star schema. These work best when time is treated as a fact, not a dimension.

[edit]Overview

The TSDS allows users to create, enumerate, update and destroy various time series and organize them in some fashion. These series may be organized hierarchically and optionally have companion metadata available with them. The server often supports a number of basic calculations that work on a series as a whole, such as multiplying, adding, or otherwise combining various time series into a new time series. They can also filter on arbitrary patterns defined by the day of the week, low value filters, high value filters, or even have the values of one series filter another. Some TSDSs also build in a wealth of statistical functions.

For example, consider the following hypothetical "time series" or "profile" expression:

SELECT nymex/gold_price * nymex/gold_volume

To analyze this, the TSDS would join the two series nymex/gold_price and nymex/gold_volume based on the overlapping areas of time for each, multiply the values where they intersect, and then output a single composite time series.

Obviously, more complex expressions are allowed. TSDSs often allow users to manage a repository of filters or masks that specify in some way a pattern based on the day of a week and a set of holidays. In this way, one can readily assemble time series data. Assuming such a filter exists, one might hypothetically write

SELECT onpeak( cellphoneusage )

which would extract out the time series of cellphoneusage that only intersects that of 'onpeak'. Some systems might generalize the filter to be a time series itself.

This syntactical simplicity drives the appeal of the TSDS. For example, a simple utility bill might be implemented using a query such as:

SELECT MAX( onpeak( powerusagekw ) ) * demand_charge;
 
SELECT SUM( onpeak( powerusagekwh ) ) * energy_charge;

TSDS also generally have conversions to and from specific time zones implemented at the server level.

[edit]Example

A workable implementation of a time series database can be easily deployed in a conventional SQL-based relational database provided that the database software supports both binary large objects (BLOBs) and user-defined functions. SQL statements that operate on one or more time series quantities on the same row of a table or join can easily be written, as the user-defined time series functions operate comfortably inside of a SELECT statement. However, time series functionality such as a SUM function operating in the context of a GROUP BY clause cannot be easily achieved.

[edit]See also

• Historian database

TimeScape QL+

Data. Decisions. Together.

This white paper describes TimeScape QL+, a new query language designed for financial markets in order to bring users, data and decision-making closer together. This language is easy to understand, easy to extend, provides powerful support for vector arithmetic of intraday and historic data and deals with data issues that are specific to the financial markets.

View complete TimeScape QL+ white paper PDF. (This document requires the free Adobe/Acrobat Reader.) 16 pages, 420KB

Introduction

More Data, Less Time

Why do we need another query language? Good question. The usage of SQL is both prevalent and effective throughout financial markets IT and the software industry as a whole. However, there are many people who need to access, manipulate and analyse data who are not technical experts in SQL but are experts in their own particular field of business.

Nowhere is this more apparent than within the financial markets - where traders, fund managers, quants, research analysts, risk managers and other business staff are under constant competitive pressure to analyse larger and larger volumes of increasingly complex data in less and less time.

Users and Technologists Think About Data Differently

Business users in financial markets tend to think about the data they need in terms of the financial instruments they trade - such as equities, bonds, options etc. Traders do not think of real-time data, time series data, tick data, static data and calculated data very differently - to them all of this data is needed and relevant to the business decisions that are being made.

However, this same data is generally split out across many tables when stored in a typical relational database implementation, rendering the data as less than user-friendly to access. The implementation gets more complex if different asset classes are stored in different databases. Even within one asset class, static terms and conditions data may be stored in different systems from time series data, tick data and calculated/derived data.

In the absence of further end-user tools to analyse the data, the trader or risk manager must understand both the SQL programming language and the table structure/architecture implemented for the databases in question. Even if the business user is capable of achieving these two things, they often do not have the time to do anything more than avoid using the database by downloading equivalent data from Open Bloomberg into Microsoft Excel.

Technologists can, of course, go some way to addressing the above issue through the provision of read-only views and wrapper functions (in the form of stored procedures) to hide some of the complexity from the end-user. However, as data demands from the business expand, and table complexity increases, these procedures can become computationally expensive and increasingly difficult to manage, maintain and change. As a result, business users continue to be heavily reliant on technologists to deliver and so the unproductive cycle continues.

White paper contents

• More Data, Less Time
• Users and Technologists Think About Data Differently
• TimeScape QL+: Designed for Financial Markets
• Functional Overview of TimeScape QL+
• Some Examples of TimeScape QL+ in Action
  • Example #1 – Loading a Price History for an Equity
  • Example #2 – Calculating Historic Volatility of an Underlying
  • Example #3 – The ‘.?’ Statement and Context Sensitive Help
  • Example #4 – Data Rules and Bond Spread Analysis
  • Example #5 – Tick Data Analysis, Data Frequency and VWAP
  • Example #6 – Multiple Instrument Analysis
  • Example #7 – Vector Arithmetic, Spread Analysis and VAR
  • Example #8 – Adding Your Own Functions
  • Example #9 – Adding Your Own Objects
• Future Directions
• Summary

High Frequency Data Analysis

Considered decision-making with TimeScape

This paper illustrates how Xenomorph’s real-time analytics and data management system, TimeScape, enables extremely rapid and extensible analysis of tick and intraday timeseries data delivering competitive advantage in pre- and post-trade decision support.

View complete High Frequency Data Analysis white paper PDF. (This document requires the free Adobe/Acrobat Reader.) 20 pages, 860KB

Introduction

A Time of Change and Opportunity

Data management in financial markets are being driven through a period of fundamental change. Trade volumes are increasing exponentially as electronic execution delivers faster trading with ever-tighter margins. The proprietary algorithms used in algorithmic and statistical arbitrage trading are becoming more complex. Developments in areas such as credit theory are establishing market relationships that motivate more complex cross-asset trading strategies. Regulations such as MiFID and Regulation NMS are pushing the whole industry towards better and more transparent execution, but are also fundamental drivers behind both huge business change and dramatically increased data volumes. All of these factors are combining to provide both profit and cost incentives to move away from single asset class data silos.

Looking at data management from a trader’s perspective, then a decade ago many practitioners were content with analysing end of day historic data for strategy back-testing and instrument pricing purposes. It should be said that they quite possibly had no choice from a technological perspective; the capture, storage and analysis of intraday data volumes even then was challenging, especially at a time when the relational database was still a relatively new technology. Given however that derivative pricing margins were wider and statistical arbitrage was profitable using end of day prices, then there was also little incentive to store and analyse intraday tick and high frequency data. Much tighter trading margins, cross-asset trading and improved technology have changed traders’ perceptions of what is required and what kind of analysis is possible with high frequency intraday data.

Risk management has previously not been greatly concerned with intraday data. As many risk managers will confirm, obtaining clean data for end of day risk measurement is challenging enough. Risk measurement techniques such as monte-carlo or historical simulation VaR require large amounts of historical data and are calculation intensive. Large data universes or poor implementation may mean that it is challenging to attempt to run these techniques as an overnight batch, let alone perform the calculations in real or near real-time. Increased intraday trading exposure, better understanding of intraday market behaviour and recent regulatory requirements concerning data transparency and data quality are driving risk managers towards more analysis of tick by tick and intraday data.

The changes above require systems that can adapt to the pace of change, delivering high performance analysis even when the quantity of intraday data being analysed is massive. This paper describes how Xenomorph’s real-time analytics and data management system, TimeScape, has been designed to meet these current challenges and to deliver competitive advantage in pre- and post-trade decision support.

White paper contents

• A Time of Change and Opportunity
• Data Transparency – Increased Productivity for All
• Tick Capture – Flexibility and Ease of Use Together
• Tick Storage – Real-Time, Intraday and History All in One
• Data Access - for Both Traders and Technologists
  • Loading Intraday Data with Functions
  • Loading Intraday Data with TimeScape QL+
• Data Validation – Why Waste Half Your Time?
  • Data Frequency and Time Snapping
  • Data Filling, Aligning and Loading Rules
  • Real or Interpolated Data?
• Data Analysis – From Backtesting to Best Execution
  • Chaining Analytical Functions
  • Intraday Time Period Analysis
  • Beyond the Spreadsheet
• Event Processing – Real-Time Analysis Automation
• Your Proprietary Advantage
• Summary

TimeScape provides a powerful database engine (TimeScape XDB) for managing vast quantities of time series data.

This data can be anything from simple numerical data (e.g. daily closing prices or rates) and intraday (tick-by-tick) data, to more complex data that may or may not have a time dimension such as dividend projections, instrument relationships, index/basket/portfolio compositions, curve compositions and volatility surfaces states.

Unlike many other time series database systems, TimeScape manages these types of data within one consistent and highly efficient database system that is scalable and easily customised to meet the ever increasing demands of the business.

In addition, it makes this data easily accessible to end-users, developers and systems alike via its powerful business orientated analysis language called TimeScape QL+.

This simple to use language has been specifically designed to bridge the gap between business users and technologists, without compromising performance. It allows highly sophisticated analysis to be constructed and utilised from the user’s environment of choice, whether that is Microsoft Excel, a TimeScape application or one that an organisation has built themselves using the TimeScape development toolkits.

In particular, TimeScape’s Tick / Time Series database:

• Comes with a user-orientated set of desktop tools for interacting, visualising and analysing the data stored within it
• Supports a wide variety of data types (including numbers, date-times, logical, text, lists, matrices, references, formulae, spreadsheet inside, binary objects)
• Supports static, semi-static (weekly, monthly), daily and tick data frequencies
• Has a very flexible data model that co-supports multiple instrument identifier systems (Reuters, Bloomberg, ISIN, Internal security systems etc.) as well as side-by-side support for multiple sources of data (Reuters, Bloomberg etc.)
• Includes a sophisticated security model that allows data access to be controlled down to a particular instrument attribute level (for example, limiting write access to US Equity Closing prices)
• Is supported either in a proprietary format or as part of SQL Server 2005/8
• Is highly scalable, with benchmarks having been completed recently on a 12Tb database containing over 750 billion ticks of data with no noticeable loss in performance

Email us to request more information about how TimeScape Tick/Time Series Database technology can help your organisation.

The IBM® Informix® TimeSeries DataBlade™ module greatly expands the functionality of your database by adding sophisticated support for managing time-series and temporal data.

A "time series" is any set of data that is accessed in sequence by time and can be processed and analyzed in a chronological order. Key features of the Informix TimeSeries DataBlade include:

• Smart data storage that enables fast data retrieval.

• Simplified design and coding to support ad-hoc queries.

• Flexibility to enable management of multiple types of time-series data.

• Calendar-based access for high performance.

• For TimeSeries DataBlade system requirements, go to the technical support page.