Basis dataDari Wikipedia bahasa Indonesia, ensiklopedia bebas
Belum Diperiksa Langsung ke: navigasi, cari Basis data (bahasa
Inggris: database), atau sering pula dieja basisdata, adalah
kumpulan informasi yang disimpan di dalam komputer secara
sistematik sehingga dapat diperiksa menggunakan suatu program
komputer untuk memperoleh informasi dari basis data tersebut.
Perangkat lunak yang digunakan untuk mengelola dan memanggil kueri
(query) basis data disebut sistem manajemen basis data (database
management system, DBMS). Sistem basis data dipelajari dalam ilmu
informasi. Istilah "basis data" berawal dari ilmu komputer.
Meskipun kemudian artinya semakin luas, memasukkan hal-hal di luar
bidang elektronika, artikel ini mengenai basis data komputer.
Catatan yang mirip dengan basis data sebenarnya sudah ada sebelum
revolusi industri yaitu dalam bentuk buku besar, kuitansi dan
kumpulan data yang berhubungan dengan bisnis. Konsep dasar dari
basis data adalah kumpulan dari catatan-catatan, atau potongan dari
pengetahuan. Sebuah basis data memiliki penjelasan terstruktur dari
jenis fakta yang tersimpan di dalamnya: penjelasan ini disebut
skema. Skema menggambarkan obyek yang diwakili suatu basis data,
dan hubungan di antara obyek tersebut. Ada banyak cara untuk
mengorganisasi skema, atau memodelkan struktur basis data: ini
dikenal sebagai model basis data atau model data. Model yang umum
digunakan sekarang adalah model relasional, yang menurut istilah
layman mewakili semua informasi dalam bentuk tabel-tabel yang
saling berhubungan dimana setiap tabel terdiri dari baris dan kolom
(definisi yang sebenarnya menggunakan terminologi matematika).
Dalam model ini, hubungan antar tabel diwakili denga menggunakan
nilai yang sama antar tabel. Model yang lain seperti model
hierarkis dan model jaringan menggunakan cara yang lebih eksplisit
untuk mewakili hubungan antar tabel. Istilah basis data mengacu
pada koleksi dari data-data yang saling berhubungan, dan perangkat
lunaknya seharusnya mengacu sebagai sistem manajemen basis data
(database management system/DBMS). Jika konteksnya sudah jelas,
banyak administrator dan programer menggunakan istilah basis data
untuk kedua arti tersebut.
DatabaseFrom Wikipedia, the free encyclopedia
Jump to: navigation, search This article is about managing and
structuring the collections of data held on computers. For a fuller
discussion of DBMS software, see database management system. For
databased content libraries, see online database. For other uses,
see Database (disambiguation). 01000 It has been suggested that
Database administrator be merged into this 90000 article or
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00
A database consists of an organized collection of data for one
or more uses, typically in digital form. One way of classifying
databases involves the type of their contents, for example:
bibliographic, document-text, statistical. Digital databases are
managed using database management systems, which store database
contents, allowing data creation and maintenance, and search and
other access.
[edit] ArchitectureDatabase architecture consists of three
levels, external, conceptual and internal. Clearly separating the
three levels was a major feature of the relational database model
that dominates 21st century databases.[1] The external level
defines how users understand the organization of the data. A single
database can have any number of views at the external level. The
internal level defines how the data is physically stored and
processed by the computing system. Internal architecture is
concerned with cost, performance, scalability and other operational
matters. The conceptual is a level of indirection between internal
and external. It provides a common view of the database that is
uncomplicated by details of how the data is stored or managed, and
that can unify the various external views into a coherent
whole.[1]
[edit] Database management systemsMain article: Database
management system
A database management system (DBMS) consists of software that
operates databases, providing storage, access, security, backup and
other facilities. Database management systems can be categorized
according to the database model that they support, such as
relational or XML, the type(s) of computer they support, such as a
server cluster or a mobile phone, the query language(s) that access
the database, such as SQL or XQuery, performance trade-offs, such
as maximum scale or maximum speed or others. Some DBMS cover more
than one entry in these categories, e.g., supporting multiple query
languages.Examples of some commonly used DBMS are MySQL,
PostgreSQL, Microsoft Access, SQL Server, FileMaker,Oracle, RDBMS,
dBASE, Clipper,FoxPro,etc. Almost every database software comes
with an Open Database Connectivity (ODBC) driver that allows the
database to integrate with other databases.[edit] Components of
DBMS
Most DBMS as of 2009 implement a relational model.[2] Other DBMS
systems, such as Object DBMS, offer specific features for more
specialized requirements. Their components are similar, but not
identical.[edit] RDBMS components
Sublanguages Relational DBMS (RDBMS) include Data Definition
Language (DDL) for defining the structure of the database, Data
Control Language (DCL) for defining security/access controls, and
Data Manipulation Language (DML) for querying and updating
data.
Interface driversThese drivers are code libraries that provide
methods to prepare statements, execute statements, fetch results,
etc. Examples include ODBC, JDBC, MySQL/PHP, FireBird/Python. SQL
engineThis component interprets and executes the DDL, DCL, and DML
statements. It includes three major components (compiler,
optimizer, and executor). Transaction engineEnsures that multiple
SQL statements either succeed or fail as a group, according to
application dictates. Relational engineRelational objects such as
Table, Index, and Referential integrity constraints are implemented
in this component. Storage engineThis component stores and
retrieves data from secondary storage, as well as managing
transaction commit and rollback, backup and recovery, etc.
[edit] ODBMS components
Object DBMS (ODBMS) has transaction and storage components that
are analogous to those in an RDBMS. Some ODBMS handle DDL, DCL and
update tasks differently. Instead of using sublanguages, they
provide APIs for these purposes. They typically include a
sublanguage and accompanying engine for processing queries with
interpretive statements analogous to but not the same as SQL.
Example object query languages are OQL, LINQ, JDOQL, JPAQL and
others. The query engine returns collections of objects instead of
relational rows.
[edit] TypesThis section does not cite any references or
sources.Please help improve this article by adding citations to
reliable sources. Unsourced material may be challenged and removed.
(August 2010)
[edit] Operational database
These databases store detailed data about the operations of an
organization. They are typically organized by subject matter,
process relatively high volumes of updates using transactions.
Essentially every major organization on earth uses such databases.
Examples include customer databases that record contact, credit,
and demographic information about a business' customers, personnel
databases that hold information such as salary, benefits, skills
data about employees, manufacturing databases that record details
about product components, parts inventory, and financial databases
that keep track of the organization's money, accounting and
financial dealings.[edit] Data warehouse
Data warehouses archive modern data from operational databases
and often from external sources such as market research firms.
Often operational data undergoes transformation on its way into the
warehouse, getting summarized, anonymized, reclassified, etc. The
warehouse becomes the central source of data for use by managers
and other end-users who may not have
access to operational data. For example, sales data might be
aggregated to weekly totals and converted from internal product
codes to use UPC codes so that it can be compared with ACNielsen
data.Some basic and essential components of data warehousing
include retrieving and analyzing data, transforming,loading and
managing data so as to make it available for further use.[edit]
Analytical database
Analysts may do their work directly against a data warehouse, or
create a separate analytic database for Online Analytical
Processing. For example, a company might extract sales records for
analyzing the effectiveness of advertising and other sales
promotions at an aggregate level.[edit] Distributed database
These are databases of local work-groups and departments at
regional offices, branch offices, manufacturing plants and other
work sites. These databases can include segments of both common
operational and common user databases, as well as data generated
and used only at a users own site.[edit] End-user database
These databases consist of data developed by individual
end-users. Examples of these are collections of documents in
spreadsheets, word processing and downloaded files, or even
managing their personal baseball card collection.[edit] External
database
These databases contain data collected for use across multiple
organizations, either freely or via subscription. The Internet
Movie Database is one example.[edit] Hypermedia databases
The Worldwide web can be thought of as a database, albeit one
spread across millions of independent computing systems. Web
browsers "process" this data one page at a time, while web crawlers
and other software provide the equivalent of database indexes to
support search and other activities.
[edit] ModelsMain article: Database model [edit] Post-relational
database models
Products offering a more general data model than the relational
model are sometimes classified as post-relational.[3] Alternate
terms include "hybrid database", "Object-enhanced RDBMS" and
others. The data model in such products incorporates relations but
is not constrained by E.F. Codd's Information Principle, which
requires thatall information in the database must be cast
explicitly in terms of values in relations and in no other
way[4]
Some of these extensions to the relational model integrate
concepts from technologies that predate the relational model. For
example, they allow representation of a directed graph with trees
on the nodes. Some post-relational products extend relational
systems with non-relational features. Others arrived in much the
same place by adding relational features to pre-relational systems.
Paradoxically, this allows products that are historically
pre-relational, such as PICK and MUMPS, to make a plausible claim
to be post-relational.[edit] Object database models Main article:
Object database
In recent years, the object-oriented paradigm has been applied
in areas such as engineering and spatial databases,
telecommunications and in various scientific domains. The
conglomeration of object oriented programming and database
technology led to this new kind of database. These databases
attempt to bring the database world and the application-programming
world closer together, in particular by ensuring that the database
uses the same type system as the application program. This aims to
avoid the overhead (sometimes referred to as the impedance
mismatch) of converting information between its representation in
the database (for example as rows in tables) and its representation
in the application program (typically as objects). At the same
time, object databases attempt to introduce key ideas of object
programming, such as encapsulation and polymorphism, into the world
of databases. A variety of these ways have been tried[by whom?] for
storing objects in a database. Some products have approached the
problem from the application-programming side, by making the
objects manipulated by the program persistent. This also typically
requires the addition of some kind of query language, since
conventional programming languages do not provide language-level
functionality for finding objects based on their information
content. Others[which?] have attacked the problem from the database
end, by defining an object-oriented data model for the database,
and defining a database programming language that allows full
programming capabilities as well as traditional query
facilities.
[edit] Storage structuresMain article: Database storage
structures
Databases may store relational tables/indexes in memory or on
hard disk in one of many forms:
ordered/unordered flat files ISAM heaps hash buckets
logically-blocked files B+ trees
The most commonly used[citation needed] are B+ trees and ISAM.
Object databases use a range of storage mechanisms. Some use
virtual memory-mapped files to make the native language (C++, Java
etc.) objects persistent. This can be highly efficient but it can
make multi-language access more difficult. Others disassemble
objects into fixed- and varying-length components that are then
clustered in fixed sized blocks on disk and reassembled into the
appropriate format on either the client or server address space.
Another popular technique involves storing the objects in tuples
(much like a relational database) which the database server then
reassembles into objects for the client.[citation needed] Other
techniques include clustering by category (such as grouping data by
month, or location), storing pre-computed query results, known as
materialized views, partitioning data by range (e.g., a data range)
or by hash. Memory management and storage topology can be important
design choices for database designers as well. Just as
normalization is used to reduce storage requirements and improve
database designs, conversely denormalization is often used to
reduce join complexity and reduce query execution time.[5]
[edit] IndexingMain article: Index (database)
Indexing is a technique for improving database performance. The
many types of index share the common property that they eliminate
the need to examine every entry when running a query. In large
databases, this can reduce query time/cost by orders of magnitude.
The simplest form of index is a sorted list of values that can be
searched using a binary search with an adjacent reference to the
location of the entry, analogous to the index in the back of a
book. The same data can have multiple indexes (an employee database
could be indexed by last name and hire date.) Indexes affect
performance, but not results. Database designers can add or remove
indexes without changing application logic, reducing maintenance
costs as the database grows and database usage evolves. Given a
particular query, the DBMS' query optimizer is responsible for
devising the most efficient strategy for finding matching data. The
optimizer decides which index or indexes to use, how to combine
data from different parts of the database, how to provide data in
the order requested, etc. Indexes can speed up data access, but
they consume space in the database, and must be updated each time
the data is altered. Indexes therefore can speed data access but
slow data maintenance. These two properties determine whether a
given index is worth the cost.
[edit] TransactionsMain article: Database transaction
This section may stray from the topic of the article into the
topic of another article, Database management system. Please help
improve this section or discuss this issue on the talk page.
As every software system, a DBMS operates in a faulty computing
environment and prone to failures of many kinds. A failure can
corrupt the respective database unless special measures are taken
to prevent this. A DBMS achieves certain levels of fault tolerance
by encapsulating in database transactions units of work (executed
programs) performed upon the respective database.[edit] The ACID
rules Main article: ACID
Most DBMS provide some form of support for transactions, which
allow multiple data items to be updated in a consistent fashion,
such that updates that are part of a transaction succeed or fail in
unison. The so-called ACID rules, summarized here, characterize
this behavior:
Atomicity: Either all the data changes in a transaction must
happen, or none of them. The transaction must be completed, or else
it must be undone (rolled back). Consistency: Every transaction
must preserve the declared consistency rules for the database.
Isolation: Two concurrent transactions cannot interfere with one
another. Intermediate results within one transaction must remain
invisible to other transactions. The most extreme form of isolation
is serializability, meaning that transactions that take place
concurrently could instead be performed in some series, without
affecting the ultimate result. Durability: Completed transactions
cannot be aborted later or their results discarded. They must
persist through (for instance) DBMS restarts.
In practice, many DBMSs allow the selective relaxation of these
rules to balance perfect behavior with optimum performance.[edit]
Concurrency control and locking Main article: Concurrency
control
Concurrency control is essential for the correctness of
transactions executed concurrently in a DBMS, which is the common
execution mode for performance reasons. The main concern and goal
of concurrency control is isolation.[edit] Isolation
Isolation refers to the ability of one transaction to see the
results of other transactions. Greater isolation typically reduces
performance and/or concurrency, leading DBMSs to provide
administrative options to reduce isolation. For example, in a
database that analyzes trends rather than looking at low-level
detail, increased performance might justify allowing readers to
see
uncommitted changes ("dirty reads".) A common way to achieve
isolation is by locking. When a transaction modifies a resource,
the DBMS stops other transactions from also modifying it, typically
by locking it. Locks also provide one method of ensuring that data
does not change while a transaction is reading it or even that it
doesn't change until a transaction that once read it has
completed.[edit] Lock types
Locks can be shared[6] or exclusive, and can lock out readers
and/or writers. Locks can be created implicitly by the DBMS when a
transaction performs an operation, or explicitly at the
transaction's request. Shared locks allow multiple transactions to
lock the same resource. The lock persists until all such
transactions complete. Exclusive locks are held by a single
transaction and prevent other transactions from locking the same
resource. Read locks are usually shared, and prevent other
transactions from modifying the resource. Write locks are
exclusive, and prevent other transactions from modifying the
resource. On some systems, write locks also prevent other
transactions from reading the resource. The DBMS implicitly locks
data when it is updated, and may also do so when it is read.
Transactions explicitly lock data to ensure that they can complete
without a deadlock or other complication. Explicit locks may be
useful for some administrative tasks.[7][8] Locking can
significantly affect database performance, especially with large
and complex transactions in highly concurrent environments.[edit]
Lock granularity
Locks can be coarse, covering an entire database, fine-grained,
covering a single data item, or intermediate covering a collection
of data such as all the rows in a RDBMS table.[edit] Deadlocks
Deadlocks occur when two transactions each require data that the
other has already locked exclusively. Deadlock detection is
performed by the DBMS, which then aborts one of the transactions
and allows the other to complete.
[edit] ReplicationMain article: Database replication
Database replication involves maintaining multiple copies of a
database on different computers, to allow more users to access it,
or to allow a secondary site to immediately take over if the
primary site stops working. Some DBMS piggyback replication on top
of their transaction logging facility, applying the primary's log
to the secondary in near real-time. Database clustering is a
related concept for handling larger databases and user communities
by employing a cluster of multiple computers to host a single
database that can use replication as part of its
approach.[9][10]
[edit] SecurityMain article: Database security
Database security denotes the system, processes, and procedures
that protect a database from unauthorized activity. DBMSs usually
enforce security through access control, auditing, and
encryption:
Access control manages who can connect to the database via
authentication and what they can do via authorization. Auditing
records information about database activity: who, what, when, and
possibly where. Encryption protects data at the lowest possible
level by storing and possibly transmitting data in an unreadable
form. The DBMS encrypts data when it is added to the database and
decrypts it when returning query results. This process can occur on
the client side of a network connection to prevent unauthorized
access at the point of use.
[edit] Confidentiality
Law and regulation governs the release of information from some
databases, protecting medical history, driving records, telephone
logs, etc. In the United Kingdom, database privacy regulation falls
under the Office of the Information Commissioner. Organizations
based in the United Kingdom and holding personal data in digital
format such as databases must register with the Office.[11]
[edit] See also
Comparison of relational database management systems Comparison
of database tools Data hierarchy Database design Database theory
Database-centric architecture Datastructure Document-oriented
database Government database In-memory database Real time
database
[edit] References1. ^a b
Date 1990, pp. 3132
2. ^ "Design of Main Memory Database System/Overview of DBMS".
En.wikibooks.org.
http://en.wikibooks.org/wiki/Design_of_Main_Memory_Database_System/Over
view_of_DBMS. Retrieved 2010-08-29. 3. ^ Introducing databases by
Stephen Chu, in Conrick, M. (2006) Health informatics: transforming
healthcare with technology, Thomson, ISBN 0-17012731-1, p. 69. 4. ^
Date, C. J. (June 1, 1999). "When's an extension not an
extension?". Intelligent Enterprise 2 (8).
http://intelligententerprise.informationweek.com/db_area/archives/1999/990106/online1.jhtml
;jsessionid=Y2UNK1QFKXMBTQE1GHRSKH4ATMY32JVN. 5. ^ Lightstone,
Teorey & Nadeau 2007 6. ^ "Information on Shared Locks".
Methodsandtools.com.
http://www.methodsandtools.com/archive/archive.php?id=83. Retrieved
2010-08-29. 7. ^ ""Locking tables and databases" (section in some
documentation / explanation from IBM)". Publib.boulder.ibm.com.
http://publib.boulder.ibm.com/infocenter/rbhelp/v6r3/index.jsp?
topic=/com.ibm.redbrick.doc6.3/wag/wag80.htm. Retrieved 2010-08-29.
8. ^ "Routine Database Maintenance". Postgresql.org.
http://www.postgresql.org/docs/8.3/static/routine-vacuuming.html.
Retrieved 2010-08-29. 9. ^ "MySQL Cluster". Mysql.com. 2010-08-25.
http://www.mysql.com/products/database/cluster/. Retrieved
2010-08-29. 10.^ "Oracle Real Application Cluster (RAC)".
Oracle.com. 2010-03-23.
http://www.oracle.com/lang/de/database/rac_home.html. Retrieved
2010-0829. 11.^ "Information Commissioner's Office". ICO.
http://www.ico.gov.uk/. Retrieved 2010-08-29.
[edit] Further reading
Ling Liu and Tamer M. zsu (Eds.) (2009). "Encyclopedia of
Database Systems, 4100 p. 60 illus. ISBN 978-0-387-49616-0. Table
of Content available at
http://refworks.springer.com/mrw/index.php?id=1217 Beynon-Davies,
P. (2004). Database Systems. 3rd Edition. Palgrave, Houndmills,
Basingstoke. Connolly, Thomas and Carolyn Begg. Database Systems.
New York: Harlow,
2002.
Date, C. J. An Introduction to Database Systems, Eighth Edition,
Addison Wesley, 2003. Date, C. J. (2003). An Introduction to
Database Systems, Fifth Edition. Addison Wesley. ISBN
0-201-51381-1. Galindo, J.; Urrutia, A.; Piattini, M. Fuzzy
Databases: Modeling, Design and Implementation (FSQL guide). Idea
Group Publishing Hershey, USA, 2006. Galindo, J., Ed. Handbook on
Fuzzy Information Processing in Databases. Hershey, PA: Information
Science Reference (an imprint of Idea Group Inc.), 2008. Gray, J.
and Reuter, A. Transaction Processing: Concepts and Techniques, 1st
edition, Morgan Kaufmann Publishers, 1992. Kroenke, David M.
Database Processing: Fundamentals, Design, and Implementation
(1997), Prentice-Hall, Inc., pages 130-144. Kroenke, David M. and
David J. Auer. Database Concepts. 3rd ed. New York: Prentice, 2007.
Lightstone, S.; Teorey, T.; Nadeau, T. (2007). Physical Database
Design: the database professional's guide to exploiting indexes,
views, storage, and more. Morgan Kaufmann Press. ISBN
0-12-369389-6. O'Brien, James. "Management Information Systems".
New York 1999 Shih, J. "Why Synchronous Parallel Transaction
Replication is Hard, But Inevitable?", white paper, 2007. Teorey,
T.; Lightstone, S. and Nadeau, T. Database Modeling & Design:
Logical Design, 4th edition, Morgan Kaufmann Press, 2005. ISBN
0-12685352-5 Tukey, John W. Exploratory Data Analysis. Reading, MA:
Addison Wesley, 1977. Manovich, Lev.Database as a Symbolic Form,
Cambridge: MIT press, 2001.
CONTOH PERANGKAT DATABASE Pengenalan DatabaseAuthor: Muhammad
Subhan Published: March 2, 2009 Category: Database Database
dibutuhkan di suatu organisasi/ perusahaan untuk memenuhi kebutuhan
para pemakai diseluruh jenjang perusahaan. Apabila kita merupakan
orang yang baru dalam dunia pekerja atau terutama baru memasuki
jenjang perkuliahan jurusan ilmu komputer, diharuskan mengenal
Database atau bahasa Indonesia nya Basis Data atau Pengantar Basis
Data. Tulisan ini merupakan pengenalan tentang database/ basis data
yang dirangkum dengan bahasa yang mudah dicerna. Dan pastinya belum
memasuki tahap, praktik dengan penggunaan program akses database.
Artikel ini hanya pengenalan Database (full teori) tidak panjang
lebar dan tidak dikit, pas dan cukup bagi seseorang untuk mengenal
database. Didalamnya terdapat arti dan keuntungan penggunaan DBMS
untuk mengelola dan mengakses database.Serta siapa saja yang
membutuhkan database??
Partisi Tabel dan Partisi Index Pada Database OracleAuthor:
Mudafiq Riyan Pratama Published: October 5, 2010 Category: Database
Oracle merupakan software database yang banyak dipakai di
perusahaan besar di seluruh dunia saat ini. Software ini juga
banyak diminati oleh para konsultas pembuat aplikasi yang berkaitan
dengan database. Sistem keamanannya yang handal membuat para
professional yang berkecimpung dalam dunia database lebih memilih
oracle sebagai perangkat untuk menunjang kegiatan bisnis mereka.
Disamping sistem security yang handal, Oracle merupakan software
database yang bisa menampung serta mengelola data dengan kapasitas
yang sangat besar serta dapat mengaksesnya dengan sangat cepat
pula. Sintaks SQL nya yang hampir seluruhnya telah memenuhi
standart ANSI-92 lebih memudahkan para programmer database dalam
membangun aplikasi baik dari sisi back end maupun dari sisi front
end. Demikian pula bagi administrator yang berkecimpung dalam
menangani administrasi database serta bertanggung jawab terhadap
keamanan database akan merasa diuntungkan serta dimudahkan dengan
software Oracle yang
lebih establish ini. Oracle telah menyediakan fitur untuk
mempartisi tabel, yang berfungsi agar tabel dapat dibagi menjadi
beberapa kriteria, contohnya kita mempunyai tabel suplier, tabel
customer, dll, guna dari partitioned table ini agar dapat
memanajemen database yang berukuran besar, sehingga menjadi
database yang lebih terstruktur. Fitur lain yang dimiliki oleh
oracle adalah index. Secara umum, index digunakan untuk mempercepat
perncarian data didalam tabel database relasional. Sebenarnya inti
dari partisi itu adalah membagi ruang untuk data-data tertentu.
Jika dikaitkan dengan DBMS, Partisi tabel adalah membagi data suatu
tabel menjadi beberapa bagian/kriteria. Misal tabel mahasiswa,
dipartisi/dibagi berdasarkan NIM. Sehingga dapat diketahui angkatan
mahasiswa berdasarkan NIMnya. Dan konsep partisi ini dapat
dikembangkan sebagai DSS (Decision Support System) untuk
pengambilan keputusan tertentu. Sedangkan index digunakan untuk
mempercepat perncarian data didalam tabel database relasional.
Sehingga dengan adanya index partition, memungkinkan untuk
meningkatkan performa pencarian data. Berikut adalah konsep umum
partisi tabel:
0100090000030053010000003584000000001610000026060f002220574d464301000000000001
00e402000000000d00000000200000f07a0100f09a0100010000006c00000000000000000000002
b010000dd00000000000000000000009e240000191b000020454d4600000100f09a01000c00000
001000000000000000000000000000000000400000003000040010000f00000000000000000000
0000000000000e2040080a90300460000002c00000020000000454d462b014001001c000000100
000000210c0db01000000600000006000000046000000ec900000e0900000454d462b224004000
c000000000000001e4009000c00000000000000244001000c00000000000000304002001000000
0040000000000803f214007000c0000000000000008400005389000002c9000000210c0db01000
000000000000000000000000000000000000100000089504e470d0a1a0a0000000d49484452000
0012c000000de080600000087503dd30000200049444154789cecbd777c5cd775e0ff9d8a199441
ef20001204418060039b281691a244c996644ab2643b4e1cdb593bf17ee2d89f75f6e3c4f6baace2c
889bdf2261baf9c44f6ca0aad4259a2d8299092091020d84010040810bdf7de06d3e7fdfec0ef5ebd
190c48b0098083f3f9e08333afdc77eebde79e76cf3b4fa3288ac20c204e69341a1f7c3ec14dc85f10
e03fb68bf8dce082af17f1f98d6bf1037f01a01650772aacd46dde0f7c21c35c33c0ede06a9a1722fd
8bf8c2c735ca1fc0ca9f0f037937f8428099d86421f56126980f3cb088cf0e9702eb7616d07c5b9062
316934f3c7c5982dbe504051143c1e0f838383c4c4c4a0d3e9e69aa47b027f0802f73f13e805723f8
4955894e2bf56abf559b41f17a8e9f07abd68b55a9fdf774b8ba228010590fa981a0ff44c7f013f5f34
9aba7f76bb9dc6c646c2c3c3d16ab5531a6f1ed076bbfc18e8d8edf040a0b9f6783c724cc4355eafd7
47b0df2e9fa9f9f64ed6a7ba0de9522d70013d2d86752b08b4a86e868bdff7d3aa502f1c357eb3eb6
63a16a89d9be1376bfb66f4deead84cc26eaef099603ed076277d0934de6a01712bdc1fd4c24ab42f
14a33f0db37dd64cb4de0c6ec5dfb74bc37cc3753ffad18f7e14a8e3334dcced487aafd78bc3e1e0f7
bfff3da5a5a5ac5cb9d26712c535fe8ca53ea6feadd68afef779bd5e9f63ea7bcacacaf07abd5cb97285
8b172f121d1d4d797939616161bcf5d65b188d46626363033e571c13fd0ef45ca7d3c9f9f3e7e9ece
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8808c6c7c7292929a1aeae8ef4f474fafafaa8afafe7faf5eb8c8c8c101b1b4b757535f5f5f594969612
191949484808d5d5d5e4e7e7e3f178888989a1a2a2829696163a3b3bf9f6b7bf8dcbe542abd5d2d5
d5456b6b2bdffbdef7d8b56b1795959524272713131343797939a5a5a5c4c6c6e272b9a8aeaea6a1
a181969616e2e2e2703a9d9c3d7b96eaea6ad2d2d2d06ab5d4d4d470eedc397ef7bbdfd1ddddcdc3
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2e3e3e9ecece4f4e9d3389d4ec96b8585855cbb768d975e7a895dbb76111f1f8fd56ae5cffffccf1918
1860fdfaf5141515515a5a4a585818353535fcdffffb7f090a0a62747494b8b8385e7ef965cacbcbd9
bd7b37454545d4d6d6121d1d8dd96c9ee6f92c045c7f33f729103e5b10b1a2eaea6aac562ba9a9a9
1c3a74886f7ce31b78bd5e6c361bffeffffd3f0a0b0b090f0f27353595bffbbbbfe37fffefff4d4545055
6ab554efc3ffff33f939e9e4e6767272fbffc323ff9c94fe8efef47afd773ead429fee22ffe829ffce4278
4878773e0c001bef7bdeff1377ff33768b55a1e7ef861eaeaeaa8adad657272929e9e1ec2c3c3e9ecec
e4faf5ebd4d5d5b171e3460e1f3eccaf7ffd6b4243433978f0205ff8c217649bbdbdbd7cfffbdf67606
08037df7c535a457bf6ece1bffff7ff8ec562a1b4b494cf7ce63300b8dd6e9a9a9a686b6ba3a4a484a3
478f121a1aca1b6fbcc177bffb5dc6c6c63871e204f9f9f95cb87081eddbb7e37038686a6ae285175e
40abd572f8f061fee11ffe819494947961c968341a8c4623e9e9e9f3829e3b0161bd1c3e7c988d1b3
772f1e245262727090e0e465114eaeaeaf8e637bf497c7c3c3d3d3d7cfdeb5f47afd7f3ab5ffd8a9090
100a0a0a78ecb1c7f8fef7bfcf8a152b888989a1b1b151f2574c4c0c1d1d1df4f5f5d1d8d82895ec8b
2fbe486b6b2ba3a3a33cfffcf378bd5e7efdeb5f131f1f4f777737478e1ce1673ffb197d7d7d58ad56f
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3478ff2b9cf7d8eebd7af73f0e041bababaf8ee77bf8bdbedc6ed7673fcf8717efbdbdf62369ba9adad
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a5393c3232c2debd7b79e18517686c6ca4bdbd9db7df7e9bbffddbbfe5e73fff39efbcf30ea5a5a5c4c
4c4f0d39ffe94c8c8489a9b9b999898e0d9679fe5e8d1a3141616d2dbdbcbba75eb484949a1a7a787
dada5a9c4e277ff9977fc90f7ff843d6af5fcff6eddb319bcd0c0d0db17efd7a929393d9bd7b373d3d3
de8f57a7ef18b5fb06fdf3ede7efb6d9a9a9a387dfa34838383fcebbffe2b5bb66ce1dcb973fcea57bf2
22d2d8d75ebd671faf4690e1c38805eafe7dffffddf79f4d147e518994c26b66cd942666626bb77ef6
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